{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/notebooks/Alpha_Llama_Stack_Post_Training.ipynb)\n",
        "\n",
        "# [Alpha] Llama Stack Post Training\n",
        "This notebook will use a real world problem (improve LLM as tax preparer) to walk through the main sets of APIs we offer with Llama stack for post training to improve the LLM performance for agentic apps (We support supervised finetune now, RLHF and knowledge distillation will come soon!).\n",
        "\n",
        "We will also showcase how to leverage existing Llama stack [inference APIs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/apis/inference/inference.py) (ollama as provider) to get the new model's output and the [eval APIs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/apis/eval/eval.py) to help you better measure the new model performance. We hope the flywheel of post-training -> eval -> inference can greatly empower agentic apps development.\n",
        "\n",
        "\n",
        "- Read more about Llama Stack: https://llama-stack.readthedocs.io/en/latest/introduction/index.html\n",
        "- Read more about post training APIs definition: https://github.com/meta-llama/llama-stack/blob/main/llama_stack/apis/post_training/post_training.py\n",
        "\n",
        "\n",
        "Resource requirement:\n",
        "- You can run this notebook with Llama 3.2 3B instruct model on Colab's **FREE** T4 GPU\n",
        "- You can run this notebook with Llama 3.1 8B instruct model on Colab's A100 GPU or any GPU types with more than 22GB memory\n",
        "- You need to spin up an ollama server on local host (will provider step by step instruction on this)\n",
        "\n",
        "> **Note**: Llama Stack post training APIs are in alpha release stage and still under heavy development\n"
      ],
      "metadata": {
        "id": "OJUobWDRvkig"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 0. Bootstrapping Llama Stack Library\n",
        "In order to run post training on the Llama models, you will need to use a post training providers. Currently, the post training APIs are powered by **torchtune** as provider.\n",
        "\n",
        "To learn more about torchtune: https://github.com/pytorch/torchtune\n",
        "\n",
        "We will use [experimental-post-training](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/templates/experimental-post-training) as the distribution template\n",
        "\n",
        "####  0.0. Prerequisite: Have an OpenAI API key\n",
        "In this showcase, we will use [braintrust](https://www.braintrust.dev/) as scoring provider for eval and it uses OpenAI model as judge model for scoring. So, you need to get an API key from [OpenAI developer platform](https://platform.openai.com/docs/overview).\n",
        "\n",
        "\n",
        "> **Note:**\n",
        "- Set the API Key in the Secrets of this notebook as `OPENAI_API_KEY`\n",
        "\n",
        "You can choose from the list of [scoring providers](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/inline/scoring) and scoring functions that fulfill your need.\n",
        "\n"
      ],
      "metadata": {
        "id": "Auh-mBgFxXY-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-omdQDXakmK5",
        "outputId": "0c1ce7f5-9b9b-49c6-dc4f-47b196d2b2e1",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/meta-llama/llama-stack.git\n",
            "  Cloning https://github.com/meta-llama/llama-stack.git (to revision hf_format_checkpointer) to /tmp/pip-req-build-j_1bxqzm\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/meta-llama/llama-stack.git /tmp/pip-req-build-j_1bxqzm\n",
            "  Running command git checkout -b hf_format_checkpointer --track origin/hf_format_checkpointer\n",
            "  Switched to a new branch 'hf_format_checkpointer'\n",
            "  Branch 'hf_format_checkpointer' set up to track remote branch 'hf_format_checkpointer' from 'origin'.\n",
            "  Resolved https://github.com/meta-llama/llama-stack.git to commit 0fb674d77bb1a84d4e2dc9825102849ea06ba17b\n",
            "  Running command git submodule update --init --recursive -q\n"
          ]
        }
      ],
      "source": [
        "!pip install git+https://github.com/meta-llama/llama-stack.git #TODO: update this after the next pkg release"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2UEqw2nM-S61",
        "outputId": "0cf7855c-a12b-4225-c930-0e882463ec01",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Installing dependencies in system Python environment\n",
            "\u001b[2mUsing Python 3.11.11 environment at: /usr\u001b[0m\n",
            "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 176ms\u001b[0m\u001b[0m\n",
            "Installing pip dependencies\n",
            "\u001b[2mUsing Python 3.11.11 environment at: /usr\u001b[0m\n",
            "\u001b[2K\u001b[2mResolved \u001b[1m130 packages\u001b[0m \u001b[2min 1.82s\u001b[0m\u001b[0m\n",
            "\u001b[2K   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "\u001b[2K\u001b[1A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "\u001b[2K\u001b[2A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[2K\u001b[3A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2K\u001b[4A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2K\u001b[4A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m\u001b[2m------------------------------\u001b[0m\u001b[0m     0 B/25.89 KiB\n",
            "\u001b[2K\u001b[5A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 14.87 KiB/25.89 KiB\n",
            "\u001b[2K\u001b[5A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 14.87 KiB/25.89 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 14.88 KiB/69.83 KiB\n",
            "\u001b[2K\u001b[6A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 14.87 KiB/25.89 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 14.88 KiB/69.83 KiB\n",
            "\u001b[2K\u001b[6A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 14.87 KiB/25.89 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m\u001b[2m------------------------------\u001b[0m\u001b[0m     0 B/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 14.88 KiB/69.83 KiB\n",
            "\u001b[2K\u001b[7A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 14.87 KiB/25.89 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 14.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 14.88 KiB/69.83 KiB\n",
            "\u001b[2K\u001b[7A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 14.87 KiB/25.89 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 14.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 14.88 KiB/69.83 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 14.87 KiB/306.28 KiB\n",
            "\u001b[2K\u001b[8A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 14.87 KiB/25.89 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 14.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 14.88 KiB/69.83 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 14.87 KiB/306.28 KiB\n",
            "\u001b[2K\u001b[8A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mchevron   \u001b[0m \u001b[32m\u001b[2m------------------------------\u001b[0m\u001b[0m     0 B/11.32 KiB\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 14.87 KiB/25.89 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 14.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 14.88 KiB/69.83 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 14.87 KiB/306.28 KiB\n",
            "\u001b[2K\u001b[9A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mchevron   \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 11.32 KiB/11.32 KiB\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 14.87 KiB/25.89 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 14.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 14.88 KiB/69.83 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 14.87 KiB/306.28 KiB\n",
            "\u001b[2K\u001b[9A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mchevron   \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 11.32 KiB/11.32 KiB\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 14.87 KiB/25.89 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 14.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 14.88 KiB/69.83 KiB\n",
            "\u001b[2momegaconf \u001b[0m \u001b[32m\u001b[2m------------------------------\u001b[0m\u001b[0m     0 B/77.64 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 14.87 KiB/306.28 KiB\n",
            "\u001b[2K\u001b[10A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mchevron   \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 11.32 KiB/11.32 KiB\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 14.87 KiB/25.89 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 14.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 14.88 KiB/69.83 KiB\n",
            "\u001b[2momegaconf \u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 14.83 KiB/77.64 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 14.87 KiB/306.28 KiB\n",
            "\u001b[2K\u001b[10A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mbraintrust-core\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 4.10 KiB/4.33 KiB\n",
            "\u001b[2mchevron   \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 11.32 KiB/11.32 KiB\n",
            "\u001b[2mollama    \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 12.90 KiB/12.90 KiB\n",
            "\u001b[2maiosqlite \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 14.91 KiB/15.42 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-http\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 14.88 KiB/16.84 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-common\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 14.88 KiB/18.31 KiB\n",
            "\u001b[2minteregular\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 23.08 KiB/23.08 KiB\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 25.89 KiB/25.89 KiB\n",
            "\u001b[2mautoevals \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 14.91 KiB/40.94 KiB\n",
            "\u001b[2mlm-format-enforcer\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 14.07 KiB/43.19 KiB\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 14.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 14.91 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 14.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 30.88 KiB/69.83 KiB\n",
            "\u001b[2momegaconf \u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 32.43 KiB/77.64 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 14.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 46.93 KiB/113.53 KiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mbraintrust-core\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 4.33 KiB/4.33 KiB\n",
            "\u001b[2mchevron   \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 11.32 KiB/11.32 KiB\n",
            "\u001b[2mollama    \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 12.90 KiB/12.90 KiB\n",
            "\u001b[2maiosqlite \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 14.91 KiB/15.42 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-http\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 14.88 KiB/16.84 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-common\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 14.88 KiB/18.31 KiB\n",
            "\u001b[2minteregular\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 23.08 KiB/23.08 KiB\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 25.89 KiB/25.89 KiB\n",
            "\u001b[2mautoevals \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 30.91 KiB/40.94 KiB\n",
            "\u001b[2mlm-format-enforcer\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 14.07 KiB/43.19 KiB\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 14.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 33.83 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 14.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 30.88 KiB/69.83 KiB\n",
            "\u001b[2momegaconf \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 48.43 KiB/77.64 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 30.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 62.93 KiB/113.53 KiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mchevron   \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 11.32 KiB/11.32 KiB\n",
            "\u001b[2mollama    \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 12.90 KiB/12.90 KiB\n",
            "\u001b[2maiosqlite \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 14.91 KiB/15.42 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-http\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 14.88 KiB/16.84 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-common\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 14.88 KiB/18.31 KiB\n",
            "\u001b[2minteregular\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 23.08 KiB/23.08 KiB\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 25.89 KiB/25.89 KiB\n",
            "\u001b[2mautoevals \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 30.91 KiB/40.94 KiB\n",
            "\u001b[2mlm-format-enforcer\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 14.07 KiB/43.19 KiB\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 14.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 33.83 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 14.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 30.88 KiB/69.83 KiB\n",
            "\u001b[2momegaconf \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 48.43 KiB/77.64 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 30.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 62.93 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 32.00 KiB/115.93 KiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mchevron   \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 11.32 KiB/11.32 KiB\n",
            "\u001b[2maiosqlite \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 14.91 KiB/15.42 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-http\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 14.88 KiB/16.84 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-common\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 14.88 KiB/18.31 KiB\n",
            "\u001b[2minteregular\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 23.08 KiB/23.08 KiB\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 25.89 KiB/25.89 KiB\n",
            "\u001b[2mautoevals \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 30.91 KiB/40.94 KiB\n",
            "\u001b[2mlm-format-enforcer\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 14.07 KiB/43.19 KiB\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 14.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 33.83 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 14.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 46.88 KiB/69.83 KiB\n",
            "\u001b[2momegaconf \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 48.43 KiB/77.64 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 30.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 62.93 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 32.00 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 48.00 KiB/140.16 KiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2maiosqlite \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 14.91 KiB/15.42 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-http\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 14.88 KiB/16.84 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-common\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 14.88 KiB/18.31 KiB\n",
            "\u001b[2minteregular\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 23.08 KiB/23.08 KiB\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 25.89 KiB/25.89 KiB\n",
            "\u001b[2mautoevals \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 30.91 KiB/40.94 KiB\n",
            "\u001b[2mlm-format-enforcer\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 14.07 KiB/43.19 KiB\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 14.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 33.83 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 14.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 46.88 KiB/69.83 KiB\n",
            "\u001b[2momegaconf \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 64.43 KiB/77.64 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 30.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 62.93 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 32.00 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 48.00 KiB/140.16 KiB\n",
            "\u001b[2K\u001b[22A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2maiosqlite \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 14.91 KiB/15.42 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-http\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 14.88 KiB/16.84 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-common\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 14.88 KiB/18.31 KiB\n",
            "\u001b[2minteregular\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 23.08 KiB/23.08 KiB\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 25.89 KiB/25.89 KiB\n",
            "\u001b[2mautoevals \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 30.91 KiB/40.94 KiB\n",
            "\u001b[2mlm-format-enforcer\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 14.07 KiB/43.19 KiB\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 14.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 33.83 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 14.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 46.88 KiB/69.83 KiB\n",
            "\u001b[2momegaconf \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 64.43 KiB/77.64 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 30.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 62.93 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 32.00 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 48.00 KiB/140.16 KiB\n",
            "\u001b[2K\u001b[22A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2maiosqlite \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 14.91 KiB/15.42 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-http\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 14.88 KiB/16.84 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-common\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 14.88 KiB/18.31 KiB\n",
            "\u001b[2minteregular\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 23.08 KiB/23.08 KiB\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 25.89 KiB/25.89 KiB\n",
            "\u001b[2mautoevals \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 30.91 KiB/40.94 KiB\n",
            "\u001b[2mlm-format-enforcer\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 14.07 KiB/43.19 KiB\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 14.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 33.83 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 14.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 46.88 KiB/69.83 KiB\n",
            "\u001b[2momegaconf \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 64.43 KiB/77.64 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 30.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 62.93 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 32.00 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 48.00 KiB/140.16 KiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2maiosqlite \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 14.91 KiB/15.42 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-http\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 14.88 KiB/16.84 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-common\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 14.88 KiB/18.31 KiB\n",
            "\u001b[2mimportlib-metadata\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 25.89 KiB/25.89 KiB\n",
            "\u001b[2mautoevals \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 30.91 KiB/40.94 KiB\n",
            "\u001b[2mlm-format-enforcer\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 30.07 KiB/43.19 KiB\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 14.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 33.83 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 30.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 46.88 KiB/69.83 KiB\n",
            "\u001b[2momegaconf \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 64.43 KiB/77.64 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 46.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 78.93 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 32.00 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 48.00 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 16.00 KiB/173.26 KiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2maiosqlite \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 14.91 KiB/15.42 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-http\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 16.84 KiB/16.84 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-common\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 14.88 KiB/18.31 KiB\n",
            "\u001b[2mautoevals \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 30.91 KiB/40.94 KiB\n",
            "\u001b[2mlm-format-enforcer\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 30.07 KiB/43.19 KiB\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 14.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 33.83 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 30.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 46.88 KiB/69.83 KiB\n",
            "\u001b[2momegaconf \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 64.43 KiB/77.64 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 46.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 78.93 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 32.00 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 48.00 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 16.00 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 79.26 KiB/255.37 KiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2maiosqlite \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 14.91 KiB/15.42 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-common\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 18.31 KiB/18.31 KiB\n",
            "\u001b[2mautoevals \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 30.91 KiB/40.94 KiB\n",
            "\u001b[2mlm-format-enforcer\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 30.07 KiB/43.19 KiB\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 14.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 33.83 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 30.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 46.88 KiB/69.83 KiB\n",
            "\u001b[2momegaconf \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 64.43 KiB/77.64 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 46.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 78.93 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 32.00 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 48.00 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 32.00 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 79.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 46.87 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 204.82 KiB/312.18 KiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2maiosqlite \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 15.42 KiB/15.42 KiB\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-common\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 18.31 KiB/18.31 KiB\n",
            "\u001b[2mautoevals \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 30.91 KiB/40.94 KiB\n",
            "\u001b[2mlm-format-enforcer\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 30.07 KiB/43.19 KiB\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 14.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 33.83 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 30.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 46.88 KiB/69.83 KiB\n",
            "\u001b[2momegaconf \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 64.43 KiB/77.64 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 46.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 78.93 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 32.00 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 48.00 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 32.00 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 79.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 46.87 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 204.82 KiB/312.18 KiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mopentelemetry-exporter-otlp-proto-common\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 18.31 KiB/18.31 KiB\n",
            "\u001b[2mautoevals \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 30.91 KiB/40.94 KiB\n",
            "\u001b[2mlm-format-enforcer\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 30.07 KiB/43.19 KiB\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 14.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 33.83 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 30.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 46.88 KiB/69.83 KiB\n",
            "\u001b[2momegaconf \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 64.43 KiB/77.64 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 46.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 78.93 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 32.00 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 62.40 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 32.00 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 79.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 46.87 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 204.82 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 178.01 KiB/473.98 KiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mautoevals \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 30.91 KiB/40.94 KiB\n",
            "\u001b[2mlm-format-enforcer\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 30.07 KiB/43.19 KiB\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 14.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 33.83 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 30.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 62.88 KiB/69.83 KiB\n",
            "\u001b[2momegaconf \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 77.64 KiB/77.64 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 46.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 78.93 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 32.00 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 62.40 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 32.00 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 79.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 52.92 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 204.82 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 178.01 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 46.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 238.68 KiB/863.02 KiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/46)\n",
            "\u001b[2mautoevals \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 30.91 KiB/40.94 KiB\n",
            "\u001b[2mlm-format-enforcer\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 43.19 KiB/43.19 KiB\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 30.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 33.83 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 30.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 62.88 KiB/69.83 KiB\n",
            "\u001b[2momegaconf \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 77.64 KiB/77.64 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 46.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 78.93 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 52.13 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 78.40 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 32.00 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 95.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 83.29 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 221.82 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 223.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 62.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 286.68 KiB/863.02 KiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (10/46)\n",
            "\u001b[2mautoevals \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 30.91 KiB/40.94 KiB\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 30.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 33.83 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 30.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 62.88 KiB/69.83 KiB\n",
            "\u001b[2momegaconf \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 77.64 KiB/77.64 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 46.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 89.19 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 52.13 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 78.40 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 32.00 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 95.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 99.29 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 237.82 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 223.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 78.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 302.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 206.91 KiB/1.35 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (10/46)\n",
            "\u001b[2mautoevals \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 40.94 KiB/40.94 KiB\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 46.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 49.83 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 46.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 69.83 KiB/69.83 KiB\n",
            "\u001b[2momegaconf \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 77.64 KiB/77.64 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 62.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 89.19 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 62.04 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 126.40 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 48.00 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 143.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 131.29 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 253.82 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 238.31 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 84.36 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 350.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 235.41 KiB/1.35 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (10/46)\n",
            "\u001b[2mautoevals \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 40.94 KiB/40.94 KiB\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 46.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 49.83 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 46.87 KiB/63.43 KiB\n",
            "\u001b[2mstarlette \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 69.83 KiB/69.83 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 62.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 89.19 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 62.04 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 126.40 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 48.00 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 143.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 131.29 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 253.82 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 238.31 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 84.36 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 366.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 235.41 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 864.00 KiB/2.88 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (10/46)\n",
            "\u001b[2mautoevals \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 40.94 KiB/40.94 KiB\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 46.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 49.83 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 62.87 KiB/63.43 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 62.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 89.19 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 62.04 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 126.40 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 48.00 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 143.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 142.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 253.82 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 238.31 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 84.36 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 382.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 235.41 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 896.00 KiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 904.56 KiB/2.99 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (10/46)\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 46.90 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 49.83 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 62.87 KiB/63.43 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 62.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 105.19 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 62.04 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 126.40 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 48.00 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 143.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 142.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 253.82 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 238.31 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 84.36 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 478.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 235.41 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 896.00 KiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 904.56 KiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 996.94 KiB/3.39 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (10/46)\n",
            "\u001b[2mopentelemetry-proto\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 54.54 KiB/54.54 KiB\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 60.85 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 62.87 KiB/63.43 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 78.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 105.19 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 78.04 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 140.16 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 63.97 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 191.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 158.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 269.82 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 271.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 100.36 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 478.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 366.91 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 1002.65 KiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 1.03 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 1.06 MiB/3.39 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (10/46)\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 60.85 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 62.87 KiB/63.43 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 78.88 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 105.19 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 78.04 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 140.16 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 63.97 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 191.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 158.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 269.82 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 271.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 100.36 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 478.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 382.91 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 1002.65 KiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 1.03 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 1.06 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 48.00 KiB/4.53 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (10/46)\n",
            "\u001b[2muvicorn   \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 60.85 KiB/60.85 KiB\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 62.87 KiB/63.43 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 92.59 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 105.19 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 94.04 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 140.16 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 63.97 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 207.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 174.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 285.82 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 287.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 100.36 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 494.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 392.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 1.20 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 1.28 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 1.29 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 64.00 KiB/4.53 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (10/46)\n",
            "\u001b[2mopentelemetry-api\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 62.87 KiB/63.43 KiB\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 92.59 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 105.19 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 94.04 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 140.16 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 63.97 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 207.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 174.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 285.82 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 287.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 100.36 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 494.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 392.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 1.20 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 1.28 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 1.29 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 64.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 1.28 MiB/13.17 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (10/46)\n",
            "\u001b[2mfastapi   \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 92.59 KiB/92.59 KiB\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 105.19 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 94.04 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 140.16 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 63.97 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 207.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 174.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 285.82 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 287.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 116.36 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 523.81 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 408.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 1.20 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 1.28 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 1.29 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 64.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 1.28 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 1.28 MiB/20.09 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (10/46)\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 113.53 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 110.04 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 140.16 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 63.97 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 207.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 190.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 301.82 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 287.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 116.36 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 542.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 408.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 1.38 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 1.39 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 1.43 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 64.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 1.45 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 1.42 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 1.45 MiB/23.50 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (10/46)\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 113.53 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 110.04 KiB/115.93 KiB\n",
            "\u001b[2mmultiprocess\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 140.16 KiB/140.16 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 79.97 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 207.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 206.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 301.82 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 287.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 116.36 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 542.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 408.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 1.50 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 1.39 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 1.48 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 64.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 1.53 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 1.48 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 1.57 MiB/23.50 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (18/46)\n",
            "\u001b[2mdill      \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 113.53 KiB/113.53 KiB\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 110.04 KiB/115.93 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 79.97 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 223.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 206.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 312.18 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 287.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 116.36 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 542.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 408.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 1.50 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 1.39 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 1.55 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 64.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 1.53 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 1.54 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 1.57 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 1.53 MiB/29.25 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (18/46)\n",
            "\u001b[2mopentelemetry-sdk\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 110.04 KiB/115.93 KiB\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 79.97 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 223.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 206.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 312.18 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 303.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 116.36 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 542.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 424.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 1.50 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 1.39 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 1.55 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 64.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 1.53 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 1.54 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 1.57 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 1.53 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 1.48 MiB/53.70 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (18/46)\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 79.97 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 239.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 206.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 312.18 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 303.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 132.36 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 574.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 440.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 1.69 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 1.73 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 1.75 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 80.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 1.71 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 1.73 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 1.77 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 1.75 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 1.67 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 1.65 MiB/122.01 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (18/46)\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 95.97 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 239.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 222.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 312.18 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 303.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 142.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 574.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 456.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 1.73 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 1.78 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 1.79 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 80.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 1.82 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 1.78 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 1.81 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 1.78 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 1.71 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 1.70 MiB/122.01 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (18/46)\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 95.97 KiB/173.26 KiB\n",
            "\u001b[2mredis     \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 255.26 KiB/255.37 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 238.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 312.18 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 319.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 159.87 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 590.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 609.21 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 2.02 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 2.07 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 2.13 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 128.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 2.10 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 2.09 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 2.13 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 2.08 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 2.01 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 2.04 MiB/122.01 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (18/46)\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 106.63 KiB/173.26 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 238.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 312.18 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 319.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 175.87 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 606.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 609.21 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 2.19 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 2.09 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 2.28 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 144.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 2.26 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 2.26 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 2.32 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 2.27 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 2.21 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 2.22 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 2.30 MiB/197.84 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (18/46)\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 122.63 KiB/173.26 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 238.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 312.18 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 319.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 188.47 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 606.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 641.21 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 2.30 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 2.40 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 2.42 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 176.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 2.40 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 2.44 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 2.47 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 2.39 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 2.41 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 2.35 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 2.41 MiB/197.84 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (18/46)\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 122.63 KiB/173.26 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 254.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 312.18 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 335.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 204.47 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 638.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 696.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 2.66 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 2.71 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 2.80 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 192.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 2.74 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 2.65 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 2.80 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 2.73 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 2.69 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 2.66 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 2.76 MiB/197.84 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (22/46)\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 122.63 KiB/173.26 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 254.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 312.18 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 335.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 204.47 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 862.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 728.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 2.75 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 2.80 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 2.88 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 207.78 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 2.83 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 2.81 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 2.89 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 2.83 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 2.80 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 2.83 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 2.87 MiB/197.84 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (22/46)\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 127.97 KiB/173.26 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 254.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 312.18 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 335.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 222.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-cuda-runtime-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 862.68 KiB/863.02 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 728.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 2.75 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.93 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 3.00 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 207.78 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 2.94 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 2.91 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 3.00 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 2.95 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 2.91 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 2.96 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 2.99 MiB/197.84 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (22/46)\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 159.97 KiB/173.26 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 254.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 312.18 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 339.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 222.91 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 763.06 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.86 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.96 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 3.06 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 207.78 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 3.01 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 3.01 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 3.10 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 3.02 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 2.99 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.02 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.08 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.10 MiB/201.66 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (22/46)\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 159.97 KiB/173.26 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 270.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 312.18 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 355.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 222.91 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 792.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.87 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.97 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 3.36 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 207.78 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 3.33 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 3.30 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 3.42 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 3.27 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 3.23 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.25 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.41 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.41 MiB/201.66 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (22/46)\n",
            "\u001b[2mopentelemetry-semantic-conventions\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 159.97 KiB/173.26 KiB\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 270.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 312.18 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 355.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 222.91 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 808.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.87 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.97 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 3.36 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 207.78 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 3.33 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 3.35 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 3.42 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 3.36 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 3.33 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.35 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.41 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.41 MiB/201.66 MiB\n",
            "\u001b[2K\u001b[23A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (22/46)\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 270.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 312.18 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 371.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 238.69 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 824.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.88 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.97 MiB/2.99 MiB\n",
            "\u001b[2mhf-transfer\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 3.39 MiB/3.39 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 223.78 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 3.49 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 3.50 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 3.60 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 3.53 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 3.45 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.52 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.56 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.58 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.63 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[22A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (22/46)\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 270.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 312.18 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 371.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 238.69 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 824.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.88 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.97 MiB/2.99 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 223.78 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 3.49 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 3.50 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 3.60 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 3.53 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 3.45 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.52 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.67 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.58 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.63 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[21A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (22/46)\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 270.28 KiB/306.28 KiB\n",
            "\u001b[2mprotobuf  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 312.18 KiB/312.18 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 371.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 238.69 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 840.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.88 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.97 MiB/2.99 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 223.78 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 3.57 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 3.61 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 3.71 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 3.64 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 3.56 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.64 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.67 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.67 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.72 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[21A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (22/46)\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 270.28 KiB/306.28 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 393.92 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 238.69 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 856.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.88 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.97 MiB/2.99 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 223.78 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 3.75 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 3.80 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 3.89 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 3.81 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 3.73 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.83 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.83 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.82 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 3.86 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[20A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (22/46)\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 270.28 KiB/306.28 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 393.92 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 238.69 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 872.56 KiB/1.35 MiB\n",
            "\u001b[2mpsycopg2-binary\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.88 MiB/2.88 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.99 MiB/2.99 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 223.78 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 4.11 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 4.12 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 4.20 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 4.16 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 4.09 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 4.09 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 4.19 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 4.17 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 4.28 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[20A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (28/46)\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 286.28 KiB/306.28 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 451.48 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 253.91 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 920.56 KiB/1.35 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.99 MiB/2.99 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 239.78 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 4.39 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 4.44 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 4.51 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 4.47 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 4.40 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 4.44 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 4.50 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 4.45 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 4.58 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[19A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (28/46)\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 286.28 KiB/306.28 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 451.48 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 253.91 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 936.56 KiB/1.35 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.99 MiB/2.99 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 239.78 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 4.55 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 4.59 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 4.67 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 4.66 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 4.54 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 4.61 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 4.68 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 4.62 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 4.73 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[19A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (28/46)\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 286.28 KiB/306.28 KiB\n",
            "\u001b[2mdatasets  \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 467.84 KiB/473.98 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 269.91 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 963.56 KiB/1.35 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.99 MiB/2.99 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 255.78 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 4.98 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 5.02 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 5.11 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 5.12 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 4.97 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 5.04 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 5.27 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 5.04 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 5.18 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[19A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (28/46)\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 286.28 KiB/306.28 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 269.91 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 1.05 MiB/1.35 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.99 MiB/2.99 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 255.78 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 5.29 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 5.37 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 5.51 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 5.45 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 5.29 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 5.40 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 5.48 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 5.34 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 5.50 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[18A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (28/46)\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 286.28 KiB/306.28 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 269.91 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 1.05 MiB/1.35 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.99 MiB/2.99 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 255.78 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 5.52 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 5.60 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 5.51 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 5.70 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 5.52 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 5.56 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 5.71 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 5.57 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 5.59 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[18A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (28/46)\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 302.28 KiB/306.28 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 280.00 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 1.16 MiB/1.35 MiB\n",
            "\u001b[2mrapidfuzz \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 2.99 MiB/2.99 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 272.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 5.97 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 6.04 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 6.11 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 6.12 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 5.96 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 6.07 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 6.15 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 5.99 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 6.21 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[18A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (30/46)\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 302.28 KiB/306.28 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 280.00 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 1.17 MiB/1.35 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 288.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 6.22 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 6.27 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 6.36 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 6.36 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 6.19 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 6.30 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 6.35 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 6.23 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 6.44 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[17A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (30/46)\n",
            "\u001b[2mdnspython \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 306.28 KiB/306.28 KiB\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 296.00 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 1.19 MiB/1.35 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 288.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 6.43 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 6.48 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 6.57 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 6.58 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 6.33 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 6.52 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 6.54 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 6.43 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 6.66 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[17A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (30/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 296.00 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 1.19 MiB/1.35 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 288.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 6.46 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 6.52 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 6.58 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 6.61 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 6.44 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 6.55 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 6.60 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 6.47 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 6.69 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[16A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (30/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 312.00 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 1.33 MiB/1.35 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 304.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 6.92 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 7.02 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 7.02 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 7.08 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 6.93 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 7.04 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 7.21 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 6.93 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 7.16 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[16A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (30/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 312.00 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 1.35 MiB/1.35 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 320.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 7.47 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 7.47 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 7.72 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 7.56 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 7.57 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 7.69 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 7.67 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 7.41 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 7.65 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[16A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (30/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 328.00 KiB/791.34 KiB\n",
            "\u001b[2mpymongo   \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 1.35 MiB/1.35 MiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 336.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 7.99 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 8.14 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 8.29 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 8.20 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 8.07 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 8.19 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 8.01 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 7.97 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 8.28 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[16A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (32/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 328.00 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 336.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 8.19 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 8.22 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 8.37 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 8.33 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 8.16 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 8.42 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 8.26 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 8.22 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 8.39 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[15A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (32/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 334.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 336.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 8.64 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 8.67 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 8.83 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 8.79 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 8.53 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 8.67 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 8.68 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 8.56 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 8.80 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[15A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (32/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 350.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 352.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 9.16 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 9.15 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 9.33 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 9.34 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 9.06 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 9.21 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 9.17 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 9.09 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 9.34 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[15A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (32/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 350.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 352.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 9.67 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 9.68 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 9.84 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 9.86 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 9.59 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 9.70 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 9.65 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 9.60 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 9.68 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[15A   \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (32/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 366.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 368.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 10.17 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 10.22 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 10.35 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 10.40 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 10.10 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 10.21 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 10.00 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 10.01 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 10.21 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[15A      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m fairscale\u001b[2m==0.4.13\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (33/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 366.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 368.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 10.17 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 10.22 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 10.35 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 10.40 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 10.10 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 10.21 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 10.17 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 10.16 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 10.63 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[14A      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m antlr4-python3-runtime\u001b[2m==4.9.3\u001b[0m\n",
            "      \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m zmq\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (33/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 382.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 368.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 10.43 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 10.52 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 10.62 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 10.63 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 10.41 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 10.50 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 10.41 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 10.43 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 10.65 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[12A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (33/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 382.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 368.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 10.87 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 10.97 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 11.04 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 11.11 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 10.80 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 10.94 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 11.03 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 10.87 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-\u001b[2m-----------------------------\u001b[0m\u001b[0m 11.08 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[12A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (33/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 398.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 384.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 11.40 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 11.44 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 11.64 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 11.70 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 11.39 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 11.46 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 11.36 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 11.39 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 11.59 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[12A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (33/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 398.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 384.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 11.88 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 11.97 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 12.14 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 12.12 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 11.91 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 11.96 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 11.86 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 11.91 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 12.11 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[12A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (34/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 414.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 400.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 12.20 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 12.46 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 12.60 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 12.50 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 12.32 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 12.40 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 12.19 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 12.39 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 12.53 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[12A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (34/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 414.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 400.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 12.72 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 12.95 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 12.91 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 13.06 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 12.71 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 12.73 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 12.86 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 12.72 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 13.02 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[12A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (34/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 414.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 416.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 13.17 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 13.45 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 13.47 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 13.59 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 13.19 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 13.27 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 13.21 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 13.24 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 13.38 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[12A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (34/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 414.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 416.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-cupti-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 13.17 MiB/13.17 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 13.85 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 14.03 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 14.16 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 13.84 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 13.88 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 13.79 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 13.84 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 13.96 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[12A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (34/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 414.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 416.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 13.85 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 14.03 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 14.16 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 13.84 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 13.88 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 13.79 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 13.84 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 13.96 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[11A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (34/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 430.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 416.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 14.40 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 14.55 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 14.67 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 14.25 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 14.37 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 14.27 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 14.36 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 14.44 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[11A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (34/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 446.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 431.78 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 14.97 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 15.08 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 15.26 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 14.86 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 15.00 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 14.67 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 14.93 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 15.04 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[11A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (34/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 446.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 447.78 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 15.59 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 15.67 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 15.59 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 15.26 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 15.61 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 15.45 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 15.57 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 15.44 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[11A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (34/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 462.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 447.78 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 16.00 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 16.22 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 16.28 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 16.02 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 16.23 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 16.17 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 16.11 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 16.17 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[11A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (35/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 462.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 464.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 16.86 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 16.95 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 17.03 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 16.63 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 16.79 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 16.84 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 16.68 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 16.76 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[11A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (35/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 478.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 480.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 17.48 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 17.49 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 17.61 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 17.27 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 17.44 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 17.35 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 17.34 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 17.24 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[11A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (35/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 478.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 496.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 18.04 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 18.14 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 18.19 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 17.89 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 17.96 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 17.82 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 17.74 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 17.98 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[11A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (35/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 478.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 496.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 18.46 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 18.51 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 18.81 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 18.31 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 18.60 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 18.55 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 18.32 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 18.60 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[11A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (35/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 494.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 512.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 19.03 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 19.28 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 19.39 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 18.92 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 19.19 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 19.19 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 19.18 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 19.09 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[11A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (35/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 494.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 528.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 19.83 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 19.91 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 19.73 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 19.70 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 19.84 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 19.76 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 19.53 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 19.86 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[11A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (35/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 510.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 544.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-nvjitlink-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 20.09 MiB/20.09 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 20.39 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 20.60 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 20.15 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 20.49 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 20.55 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 20.31 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 20.64 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[11A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (35/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 510.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 560.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 20.97 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 21.11 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 20.95 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 21.02 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 21.14 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 20.89 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 21.17 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[10A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (35/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 510.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 560.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 20.97 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 21.17 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 20.95 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 21.02 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 21.14 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 20.89 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 21.17 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[10A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (35/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 510.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 576.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 21.43 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 21.79 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 21.59 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 21.67 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 21.62 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 21.37 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 21.60 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[10A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (35/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 526.80 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 576.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 22.13 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 22.30 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 22.15 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 22.00 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 22.30 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 22.02 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 22.27 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[10A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (35/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 526.80 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 592.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 22.68 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 22.91 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 22.57 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 22.71 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 22.69 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 22.55 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--\u001b[2m----------------------------\u001b[0m\u001b[0m 22.83 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[10A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (35/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 526.80 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 592.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 23.22 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 23.55 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 23.14 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 23.19 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 23.30 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 23.28 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 23.37 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[10A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (36/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 542.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 608.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 23.50 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 23.96 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 23.92 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 23.96 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 24.04 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 23.78 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 24.07 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[10A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (36/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 542.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 624.00 KiB/4.53 MiB\n",
            "\u001b[2mnvidia-cuda-nvrtc-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 23.50 MiB/23.50 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 24.68 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 24.31 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 24.40 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 24.64 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 24.42 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 24.73 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[10A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (36/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 542.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 640.00 KiB/4.53 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 24.68 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 24.57 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 24.61 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 24.64 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 24.42 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 24.73 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[9A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (36/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 558.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 640.00 KiB/4.53 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 25.28 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 25.10 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 25.22 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 25.43 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 24.89 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 25.49 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[9A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (36/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 558.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 656.00 KiB/4.53 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 26.09 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 25.90 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 25.98 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 25.93 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 25.65 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 26.07 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[9A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (37/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 558.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 672.00 KiB/4.53 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 26.75 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 26.50 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 26.63 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 26.64 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 26.29 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 26.69 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[9A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (37/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 558.91 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 672.00 KiB/4.53 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 27.39 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 27.06 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 27.56 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 27.38 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 26.96 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 27.27 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[9A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (37/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 574.36 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 1.20 MiB/4.53 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 28.06 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 27.86 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 28.10 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 27.92 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 27.46 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 27.91 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[9A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (37/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 574.36 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 2.62 MiB/4.53 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 28.48 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 28.12 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 28.39 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 28.29 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 27.95 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 28.39 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[9A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (37/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 574.36 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 3.06 MiB/4.53 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 28.89 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 28.85 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 29.05 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 29.01 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 28.65 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 29.14 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[9A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (37/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 574.36 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 3.97 MiB/4.53 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 28.95 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 29.50 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 29.70 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 29.59 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 29.27 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 29.14 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[9A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (37/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 574.36 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 4.11 MiB/4.53 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 29.06 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 30.16 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 30.38 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 30.25 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 29.88 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 30.38 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[9A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (37/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 574.36 KiB/791.34 KiB\n",
            "\u001b[2mtorchao   \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 4.53 MiB/4.53 MiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 29.14 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 30.96 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 31.16 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 31.00 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 30.71 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 31.13 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[9A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (37/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 574.36 KiB/791.34 KiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 29.14 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 30.96 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 31.16 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 31.00 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 30.71 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 31.13 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[8A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (37/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 590.36 KiB/791.34 KiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 29.14 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 31.58 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 31.88 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 31.72 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 31.43 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 31.94 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[8A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (37/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 590.36 KiB/791.34 KiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 29.22 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 32.54 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 32.69 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 32.60 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 32.26 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 32.65 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[8A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (37/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 590.36 KiB/791.34 KiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 29.22 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 33.41 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 33.53 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 33.30 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 33.06 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 33.45 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[8A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (37/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 590.36 KiB/791.34 KiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 29.24 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 34.17 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 34.49 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 34.01 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 34.00 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 34.31 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[8A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (38/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 606.91 KiB/791.34 KiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 29.24 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 35.10 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 35.30 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 34.94 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 34.77 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 35.24 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[8A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (38/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 606.91 KiB/791.34 KiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 29.25 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 35.90 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 36.08 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 35.74 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 35.55 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 36.07 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[8A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (38/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 606.91 KiB/791.34 KiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 29.25 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 36.64 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 36.90 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 36.57 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 36.32 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 36.89 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[8A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (38/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 606.91 KiB/791.34 KiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 29.25 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 37.42 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 37.66 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 37.39 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 37.06 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 37.73 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[8A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (38/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 606.91 KiB/791.34 KiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 29.25 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 38.42 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 38.63 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 38.37 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 38.05 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 38.75 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[8A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (38/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 622.91 KiB/791.34 KiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 29.25 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 39.42 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 39.52 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 39.63 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 38.97 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 39.58 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[8A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (38/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 622.91 KiB/791.34 KiB\n",
            "\u001b[2mfaiss-cpu \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 29.25 MiB/29.25 MiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 40.26 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 41.12 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 40.17 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 39.77 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 41.10 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[8A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (38/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 622.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 40.26 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 41.12 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 40.74 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 40.61 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 41.10 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[7A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (38/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 622.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 41.60 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 41.62 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 41.26 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 41.40 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 41.59 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[7A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (39/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 622.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 42.54 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 42.89 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 42.18 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 42.32 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 42.73 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[7A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (39/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 638.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 43.25 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 43.59 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 43.19 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 43.16 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 44.01 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[7A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (39/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 638.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 44.29 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 44.47 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 44.02 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 44.59 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 44.46 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[7A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (39/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 638.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 45.21 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 45.44 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 45.48 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 45.42 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----\u001b[2m--------------------------\u001b[0m\u001b[0m 45.48 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[7A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (39/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 638.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 46.08 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 46.27 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 45.91 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 46.41 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 46.43 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[7A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (39/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 638.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 47.31 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 47.15 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 47.24 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 47.16 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 47.86 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[7A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (39/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 638.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 48.17 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 48.56 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 48.22 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 47.52 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 48.01 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[7A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (39/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 670.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 49.04 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 49.39 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 48.91 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 48.33 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 48.78 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[7A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (39/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 686.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 49.36 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 49.81 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 49.32 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 49.23 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 49.89 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[7A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (39/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 702.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 50.36 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 50.65 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 50.25 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 50.09 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 50.77 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[7A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (39/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 702.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 50.89 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 51.74 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 51.17 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 51.22 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 51.33 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[7A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (39/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 718.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 52.23 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 52.52 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 51.40 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 51.97 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 51.91 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[7A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (39/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 750.91 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 52.65 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 52.94 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 52.72 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 52.42 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 53.10 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[7A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (39/46)\n",
            "\u001b[2mtorchtune \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 791.34 KiB/791.34 KiB\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 53.62 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 53.64 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 53.48 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 53.24 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 53.94 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[7A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (39/46)\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 53.62 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 53.78 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 53.48 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 53.24 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 53.94 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[6A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (39/46)\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 53.68 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 54.53 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 54.27 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 53.92 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 54.89 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[6A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (39/46)\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 53.70 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 55.87 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 55.57 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 55.27 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 55.86 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[6A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (40/46)\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 53.70 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 56.72 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 56.42 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 56.05 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 56.68 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[6A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (40/46)\n",
            "\u001b[2mnvidia-curand-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 53.70 MiB/53.70 MiB\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 57.77 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 57.32 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 57.11 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 57.63 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[6A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (40/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 57.77 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 57.32 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 57.11 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----\u001b[2m-------------------------\u001b[0m\u001b[0m 57.63 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (40/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 58.79 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 58.37 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 57.91 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 58.42 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (40/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 59.88 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 59.26 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 58.94 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 59.50 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 60.90 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 60.20 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 60.02 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 60.48 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 61.87 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 61.40 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 60.78 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 61.55 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 62.71 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 62.44 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 61.98 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 62.51 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 63.83 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 63.45 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 62.87 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 63.27 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 64.82 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 64.50 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 63.98 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 64.29 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 65.92 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 65.46 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 64.95 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 65.39 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 66.88 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 66.45 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 65.97 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 66.55 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 67.94 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 67.45 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 66.96 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 67.37 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 68.92 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 68.44 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 68.01 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 68.18 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 69.89 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 69.47 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 69.13 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 68.71 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 70.79 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 70.36 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 69.77 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 70.14 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 72.17 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 71.70 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 71.14 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 70.45 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 72.59 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 72.10 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 71.45 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 72.40 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 73.61 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 73.02 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 72.48 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 72.76 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 74.56 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 74.06 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 73.46 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 73.45 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 75.58 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 74.98 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 74.50 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 73.89 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 76.53 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 76.02 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 75.40 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 74.59 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 77.33 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 76.83 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 76.20 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 76.15 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 78.36 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 77.74 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 77.18 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 77.04 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 79.33 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 78.67 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 78.23 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 78.18 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 80.30 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 79.73 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 79.18 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 79.16 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 81.41 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 80.73 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 80.10 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------\u001b[2m-----------------------\u001b[0m\u001b[0m 80.23 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 82.45 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 81.65 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 81.10 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 81.83 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 83.38 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 82.72 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 82.11 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 83.10 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 84.38 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 83.78 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 83.69 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 83.22 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 85.97 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 85.23 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 84.72 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 84.21 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 87.32 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 85.84 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 85.17 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 86.02 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 87.99 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 86.87 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 86.14 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 86.92 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 88.94 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 87.80 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 87.21 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 87.90 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 89.19 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 89.57 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 88.97 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 88.30 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 90.75 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 90.62 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 89.19 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 90.02 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 91.95 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 91.53 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 90.26 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 90.86 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 93.04 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 91.83 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 91.90 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------\u001b[2m----------------------\u001b[0m\u001b[0m 91.93 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 94.05 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 93.53 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 92.79 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 93.02 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 94.68 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 94.59 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 93.94 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 93.94 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 96.14 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 94.98 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 95.04 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 94.86 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 97.21 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 96.72 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 96.25 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 95.79 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 98.23 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 97.89 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 97.31 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 97.24 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 99.42 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 99.02 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 98.43 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 98.45 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 100.53 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 99.94 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 99.30 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 99.73 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 101.99 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 101.87 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 100.32 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 100.15 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 102.49 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 102.42 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 102.30 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 101.86 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 104.25 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 104.00 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 103.21 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 102.06 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 105.21 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 105.05 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 104.35 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------\u001b[2m---------------------\u001b[0m\u001b[0m 103.78 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 106.45 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 106.08 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 105.57 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 104.99 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 107.53 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 107.15 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 107.17 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 105.94 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 108.48 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 108.29 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 107.68 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 107.75 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 109.51 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 109.23 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 108.62 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 108.73 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 111.24 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 111.00 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 109.60 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 109.68 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 112.33 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 111.97 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 110.57 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 110.66 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 113.42 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 113.04 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 112.14 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 111.75 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 114.49 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 113.97 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 113.37 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 112.72 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 115.65 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 115.06 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 114.38 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 113.70 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 116.90 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 116.29 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 115.61 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------\u001b[2m--------------------\u001b[0m\u001b[0m 114.98 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 117.82 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 117.22 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 116.56 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 116.06 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 119.04 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 118.33 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 117.70 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 117.56 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 120.77 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 119.40 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 118.60 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 118.83 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 121.02 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 121.20 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 120.45 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 119.23 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 122.00 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 122.50 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 121.72 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 121.19 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 122.01 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 123.98 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 123.25 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 121.92 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cusolver-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 122.01 MiB/122.01 MiB\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 125.70 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 124.45 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 124.15 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[5A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 125.98 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 125.08 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 124.39 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 127.08 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 126.09 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 125.28 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 128.57 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 127.62 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 126.64 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (41/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 129.70 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 128.96 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 128.61 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 131.02 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 131.15 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 130.30 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 133.16 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 132.33 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 131.56 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 134.34 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 133.48 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 132.95 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 136.10 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 135.06 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 133.68 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 137.28 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 136.71 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 135.67 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 138.70 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 137.15 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 139.98 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 139.90 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 138.24 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 142.14 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 142.11 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 139.58 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 143.23 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 143.34 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 141.80 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 144.59 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 144.57 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 143.11 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 145.94 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 146.20 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 144.62 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 147.50 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 148.03 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 145.94 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 149.67 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 148.90 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 147.15 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 150.94 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 150.56 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 148.48 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 152.30 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 152.26 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------------\u001b[2m-----------------\u001b[0m\u001b[0m 149.95 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 153.64 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 153.69 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 151.68 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 155.63 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 155.79 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 153.00 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 156.76 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 157.25 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 154.75 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 158.48 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 157.86 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 156.42 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 159.28 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 160.33 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 158.51 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 160.80 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 161.78 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 159.96 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 162.01 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 163.08 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------------\u001b[2m----------------\u001b[0m\u001b[0m 161.12 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 163.44 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 165.42 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 162.50 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 164.39 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 165.79 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 164.61 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 166.35 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 167.65 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 165.73 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 167.46 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 168.91 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 166.74 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 168.96 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 170.23 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 168.22 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 170.18 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 171.67 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 169.40 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 171.42 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 173.62 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 170.78 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 173.68 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 173.98 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------------\u001b[2m---------------\u001b[0m\u001b[0m 172.66 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 174.92 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 175.22 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 173.76 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 176.18 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 177.48 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 175.46 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 177.22 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 178.80 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 176.89 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 178.62 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 180.31 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 178.11 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 179.84 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 182.25 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 179.43 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 181.11 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 182.89 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 180.70 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 182.34 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 185.19 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 182.88 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 183.82 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 186.40 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------------\u001b[2m--------------\u001b[0m\u001b[0m 184.10 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 185.87 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 187.66 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 185.20 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 187.34 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 189.01 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 186.50 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 188.38 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 190.54 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 187.92 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 190.16 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 191.76 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 189.24 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 192.19 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 193.04 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 190.51 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 192.72 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 195.42 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 192.53 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 194.73 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 196.74 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 192.73 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 196.02 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 197.83 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 195.00 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 197.34 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 199.49 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 196.17 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 197.78 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 201.33 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 198.07 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cusparse-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 197.78 MiB/197.84 MiB\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 201.65 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 200.59 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[4A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cufft-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 201.66 MiB/201.66 MiB\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 200.95 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------------\u001b[2m------------\u001b[0m\u001b[0m 200.95 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
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            "\u001b[2K\u001b[2A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
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            "\u001b[2K\u001b[2A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 210.62 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
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            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------------------\u001b[2m-----------\u001b[0m\u001b[0m 218.33 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 219.95 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 223.39 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 225.76 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 228.03 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------------------\u001b[2m----------\u001b[0m\u001b[0m 231.06 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 233.54 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 235.76 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 238.47 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------------------\u001b[2m---------\u001b[0m\u001b[0m 241.31 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 243.86 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 246.53 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 248.89 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 251.52 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 253.44 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 255.44 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 258.20 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 260.73 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------------------\u001b[2m-------\u001b[0m\u001b[0m 264.23 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 266.15 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (42/46)\n",
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            "\u001b[2K\u001b[2A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (44/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 271.76 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (44/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 273.90 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (44/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------------------\u001b[2m------\u001b[0m\u001b[0m 277.05 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-------------------------\u001b[2m-----\u001b[0m\u001b[0m 281.51 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 289.38 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 291.48 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠇\u001b[0m \u001b[2mPreparing packages...\u001b[0m (44/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 293.73 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m--------------------------\u001b[2m----\u001b[0m\u001b[0m 299.00 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 303.00 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (44/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 305.36 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠋\u001b[0m \u001b[2mPreparing packages...\u001b[0m (44/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m---------------------------\u001b[2m---\u001b[0m\u001b[0m 308.32 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (44/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 313.29 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 316.47 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m----------------------------\u001b[2m--\u001b[0m\u001b[0m 321.78 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 325.80 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (44/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 330.64 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (44/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m-----------------------------\u001b[2m-\u001b[0m\u001b[0m 334.58 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 339.81 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (44/46)\n",
            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 343.72 MiB/346.60 MiB\n",
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            "\u001b[2mnvidia-cublas-cu12\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 346.59 MiB/346.60 MiB\n",
            "\u001b[2K\u001b[2A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (44/46)\n",
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            "\u001b[2K\u001b[1A\u001b[37m⠸\u001b[0m \u001b[2mPreparing packages...\u001b[0m (45/46)\n",
            "\u001b[2K\u001b[1A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (45/46)\n",
            "\u001b[2K\u001b[1A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (45/46)\n",
            "\u001b[2K\u001b[1A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (45/46)\n",
            "\u001b[2K\u001b[1A\u001b[37m⠼\u001b[0m \u001b[2mPreparing packages...\u001b[0m (45/46)\n",
            "\u001b[2K\u001b[1A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (45/46)\n",
            "\u001b[2K\u001b[1A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (45/46)\n",
            "\u001b[2K\u001b[1A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (45/46)\n",
            "\u001b[2K\u001b[1A\u001b[37m⠴\u001b[0m \u001b[2mPreparing packages...\u001b[0m (45/46)\n",
            "\u001b[2K\u001b[1A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (45/46)\n",
            "\u001b[2K\u001b[1A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (45/46)\n",
            "\u001b[2K\u001b[1A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (45/46)\n",
            "\u001b[2K\u001b[1A\u001b[37m⠦\u001b[0m \u001b[2mPreparing packages...\u001b[0m (45/46)\n",
            "\u001b[2K\u001b[1A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (45/46)\n",
            "\u001b[2K\u001b[1A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (45/46)\n",
            "\u001b[2K\u001b[1A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (45/46)\n",
            "\u001b[2K\u001b[1A\u001b[37m⠧\u001b[0m \u001b[2mPreparing packages...\u001b[0m (45/46)\n",
            "\u001b[2K\u001b[2mPrepared \u001b[1m46 packages\u001b[0m \u001b[2min 15.86s\u001b[0m\u001b[0m\n",
            "\u001b[2mUninstalled \u001b[1m15 packages\u001b[0m \u001b[2min 291ms\u001b[0m\u001b[0m\n",
            "\u001b[2K\u001b[2mInstalled \u001b[1m46 packages\u001b[0m \u001b[2min 20ms\u001b[0m\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1maiosqlite\u001b[0m\u001b[2m==0.21.0\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mantlr4-python3-runtime\u001b[0m\u001b[2m==4.9.3\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mautoevals\u001b[0m\u001b[2m==0.0.120\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mbraintrust-core\u001b[0m\u001b[2m==0.0.58\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mchevron\u001b[0m\u001b[2m==0.14.0\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mdatasets\u001b[0m\u001b[2m==3.3.2\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mdill\u001b[0m\u001b[2m==0.3.8\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mdnspython\u001b[0m\u001b[2m==2.7.0\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mfairscale\u001b[0m\u001b[2m==0.4.13\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mfaiss-cpu\u001b[0m\u001b[2m==1.10.0\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mfastapi\u001b[0m\u001b[2m==0.115.8\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mhf-transfer\u001b[0m\u001b[2m==0.1.9\u001b[0m\n",
            " \u001b[31m-\u001b[39m \u001b[1mimportlib-metadata\u001b[0m\u001b[2m==8.6.1\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mimportlib-metadata\u001b[0m\u001b[2m==8.5.0\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1minteregular\u001b[0m\u001b[2m==0.3.3\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mlevenshtein\u001b[0m\u001b[2m==0.26.1\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mlm-format-enforcer\u001b[0m\u001b[2m==0.10.10\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mmultiprocess\u001b[0m\u001b[2m==0.70.16\u001b[0m\n",
            " \u001b[31m-\u001b[39m \u001b[1mnvidia-cublas-cu12\u001b[0m\u001b[2m==12.5.3.2\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mnvidia-cublas-cu12\u001b[0m\u001b[2m==12.4.5.8\u001b[0m\n",
            " \u001b[31m-\u001b[39m \u001b[1mnvidia-cuda-cupti-cu12\u001b[0m\u001b[2m==12.5.82\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mnvidia-cuda-cupti-cu12\u001b[0m\u001b[2m==12.4.127\u001b[0m\n",
            " \u001b[31m-\u001b[39m \u001b[1mnvidia-cuda-nvrtc-cu12\u001b[0m\u001b[2m==12.5.82\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mnvidia-cuda-nvrtc-cu12\u001b[0m\u001b[2m==12.4.127\u001b[0m\n",
            " \u001b[31m-\u001b[39m \u001b[1mnvidia-cuda-runtime-cu12\u001b[0m\u001b[2m==12.5.82\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mnvidia-cuda-runtime-cu12\u001b[0m\u001b[2m==12.4.127\u001b[0m\n",
            " \u001b[31m-\u001b[39m \u001b[1mnvidia-cudnn-cu12\u001b[0m\u001b[2m==9.3.0.75\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mnvidia-cudnn-cu12\u001b[0m\u001b[2m==9.1.0.70\u001b[0m\n",
            " \u001b[31m-\u001b[39m \u001b[1mnvidia-cufft-cu12\u001b[0m\u001b[2m==11.2.3.61\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mnvidia-cufft-cu12\u001b[0m\u001b[2m==11.2.1.3\u001b[0m\n",
            " \u001b[31m-\u001b[39m \u001b[1mnvidia-curand-cu12\u001b[0m\u001b[2m==10.3.6.82\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mnvidia-curand-cu12\u001b[0m\u001b[2m==10.3.5.147\u001b[0m\n",
            " \u001b[31m-\u001b[39m \u001b[1mnvidia-cusolver-cu12\u001b[0m\u001b[2m==11.6.3.83\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mnvidia-cusolver-cu12\u001b[0m\u001b[2m==11.6.1.9\u001b[0m\n",
            " \u001b[31m-\u001b[39m \u001b[1mnvidia-cusparse-cu12\u001b[0m\u001b[2m==12.5.1.3\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mnvidia-cusparse-cu12\u001b[0m\u001b[2m==12.3.1.170\u001b[0m\n",
            " \u001b[31m-\u001b[39m \u001b[1mnvidia-nvjitlink-cu12\u001b[0m\u001b[2m==12.5.82\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mnvidia-nvjitlink-cu12\u001b[0m\u001b[2m==12.4.127\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mollama\u001b[0m\u001b[2m==0.4.7\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1momegaconf\u001b[0m\u001b[2m==2.3.0\u001b[0m\n",
            " \u001b[31m-\u001b[39m \u001b[1mopentelemetry-api\u001b[0m\u001b[2m==1.16.0\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mopentelemetry-api\u001b[0m\u001b[2m==1.30.0\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mopentelemetry-exporter-otlp-proto-common\u001b[0m\u001b[2m==1.30.0\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mopentelemetry-exporter-otlp-proto-http\u001b[0m\u001b[2m==1.30.0\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mopentelemetry-proto\u001b[0m\u001b[2m==1.30.0\u001b[0m\n",
            " \u001b[31m-\u001b[39m \u001b[1mopentelemetry-sdk\u001b[0m\u001b[2m==1.16.0\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mopentelemetry-sdk\u001b[0m\u001b[2m==1.30.0\u001b[0m\n",
            " \u001b[31m-\u001b[39m \u001b[1mopentelemetry-semantic-conventions\u001b[0m\u001b[2m==0.37b0\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mopentelemetry-semantic-conventions\u001b[0m\u001b[2m==0.51b0\u001b[0m\n",
            " \u001b[31m-\u001b[39m \u001b[1mprotobuf\u001b[0m\u001b[2m==4.25.6\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mprotobuf\u001b[0m\u001b[2m==5.29.3\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mpsycopg2-binary\u001b[0m\u001b[2m==2.9.10\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mpymongo\u001b[0m\u001b[2m==4.11.1\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mrapidfuzz\u001b[0m\u001b[2m==3.12.1\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mredis\u001b[0m\u001b[2m==5.2.1\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mstarlette\u001b[0m\u001b[2m==0.45.3\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mtorchao\u001b[0m\u001b[2m==0.8.0\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mtorchtune\u001b[0m\u001b[2m==0.5.0\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1muvicorn\u001b[0m\u001b[2m==0.34.0\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mxxhash\u001b[0m\u001b[2m==3.5.0\u001b[0m\n",
            " \u001b[32m+\u001b[39m \u001b[1mzmq\u001b[0m\u001b[2m==0.0.0\u001b[0m\n",
            "\u001b[32mBuild Successful!\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!llama stack build --template experimental-post-training --image-type venv --image-name __system__"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 0.1. spin up ollama server\n",
        "\n",
        "We need to spin up an [ollama](https://github.com/ollama/ollama) server on local host to run the inference and eval\n",
        "\n",
        "First we install xterm so that we can run command line tools"
      ],
      "metadata": {
        "id": "Of1Hd4JrnVjG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install uv colab-xterm\n",
        "%load_ext colabxterm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "4Fh9_nyRnbEO",
        "outputId": "44d03406-63bb-4b4b-b513-a2381a859bf4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting uv\n",
            "  Downloading uv-0.6.3-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n",
            "Collecting colab-xterm\n",
            "  Downloading colab_xterm-0.2.0-py3-none-any.whl.metadata (1.2 kB)\n",
            "Requirement already satisfied: ptyprocess~=0.7.0 in /usr/local/lib/python3.11/dist-packages (from colab-xterm) (0.7.0)\n",
            "Requirement already satisfied: tornado>5.1 in /usr/local/lib/python3.11/dist-packages (from colab-xterm) (6.4.2)\n",
            "Downloading uv-0.6.3-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.2 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.2/16.2 MB\u001b[0m \u001b[31m107.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading colab_xterm-0.2.0-py3-none-any.whl (115 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.6/115.6 kB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: uv, colab-xterm\n",
            "Successfully installed colab-xterm-0.2.0 uv-0.6.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!curl https://ollama.ai/install.sh | sh"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "ItLVjPBwnd3W",
        "outputId": "5e2f3455-862d-45e3-8588-a998277c18e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100 13269    0 13269    0     0  37986      0 --:--:-- --:--:-- --:--:-- 38020\n",
            ">>> Installing ollama to /usr/local\n",
            ">>> Downloading Linux amd64 bundle\n",
            "############################################################################################# 100.0%\n",
            ">>> Creating ollama user...\n",
            ">>> Adding ollama user to video group...\n",
            ">>> Adding current user to ollama group...\n",
            ">>> Creating ollama systemd service...\n",
            "\u001b[1m\u001b[31mWARNING:\u001b[m systemd is not running\n",
            "\u001b[1m\u001b[31mWARNING:\u001b[m Unable to detect NVIDIA/AMD GPU. Install lspci or lshw to automatically detect and install GPU dependencies.\n",
            ">>> The Ollama API is now available at 127.0.0.1:11434.\n",
            ">>> Install complete. Run \"ollama\" from the command line.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, run xterm to run ollama as an independent process that stays alive. We choose Llama3.2 3B Instruct model for our tax preparation task, so we need to run llama3.2 3b instruct model on ollama\n",
        "\n",
        "\n",
        "```\n",
        "ollama serve &\n",
        "ollama run llama3.2:3b --keepalive 120m\n",
        "```"
      ],
      "metadata": {
        "id": "tYaAsuvLnzwh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%xterm\n",
        "\n",
        "# ollama serve &\n",
        "# ollama run llama3.2:3b --keepalive 120m"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "https://localhost:10000/": {
              "data": "PCFkb2N0eXBlIGh0bWw+PGh0bWw+PGhlYWQ+PG1ldGEgY2hhcnNldD0idXRmLTgiLz48c2NyaXB0IGRlZmVyPSJkZWZlciIgc3JjPSJtYWluLmpzIj48L3NjcmlwdD48L2hlYWQ+PGJvZHk+PGRpdiBpZD0idGVybWluYWwiPjwvZGl2PjwvYm9keT48L2h0bWw+",
              "ok": true,
              "headers": [
                [
                  "content-length",
                  "147"
                ],
                [
                  "content-type",
                  "text/html; charset=UTF-8"
                ]
              ],
              "status": 200,
              "status_text": ""
            },
            "https://localhost:10000/main.js": {
              "data": "[encoded data, 568860 characters]",
              "ok": true,
              "headers": [
                [
                  "content-length",
                  "426644"
                ],
                [
                  "content-type",
                  "text/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            },
            "https://localhost:10000/resize?rows=43&cols=194": {
              "data": "",
              "ok": true,
              "headers": [
                [
                  "content-length",
                  "0"
                ],
                [
                  "content-type",
                  "text/html; charset=UTF-8"
                ]
              ],
              "status": 200,
              "status_text": ""
            },
            "https://localhost:10000/out": {
              "data": "W0dJTl0gMjAyNS8wMi8yNiAtIDAwOjUwOjI4IHwbWzk3OzQybSAyMDAgG1swbXwgICAgICA0My4wNjHCtXMgfCAgICAgICAxMjcuMC4wLjEgfBtbOTc7NDVtIEhFQUQgICAgG1swbSAiLyINCg==",
              "ok": true,
              "headers": [
                [
                  "content-length",
                  "109"
                ],
                [
                  "content-type",
                  "text/html; charset=UTF-8"
                ]
              ],
              "status": 200,
              "status_text": ""
            },
            "https://localhost:10000/in/G1syMDB+b2xsYW1hIHNlcnZlICYbWzIwMX4=": {
              "data": "",
              "ok": true,
              "headers": [
                [
                  "content-length",
                  "0"
                ],
                [
                  "content-type",
                  "text/html; charset=UTF-8"
                ]
              ],
              "status": 200,
              "status_text": ""
            },
            "https://localhost:10000/in/DQ==": {
              "data": "",
              "ok": true,
              "headers": [
                [
                  "content-length",
                  "0"
                ],
                [
                  "content-type",
                  "text/html; charset=UTF-8"
                ]
              ],
              "status": 200,
              "status_text": ""
            },
            "https://localhost:10000/in/G1syMDB+b2xsYW1hIHJ1biBsbGFtYTMuMjozYiAtLWtlZXBhbGl2ZSAxMjBtG1syMDF+": {
              "data": "",
              "ok": true,
              "headers": [
                [
                  "content-length",
                  "0"
                ],
                [
                  "content-type",
                  "text/html; charset=UTF-8"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 839
        },
        "collapsed": true,
        "id": "ah6Rl2y_n4Ni",
        "outputId": "fc5ff43b-baf3-4cd5-94ae-097be20b948d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Launching Xterm..."
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        (async () => {\n",
              "            const url = new URL(await google.colab.kernel.proxyPort(10000, {'cache': true}));\n",
              "            const iframe = document.createElement('iframe');\n",
              "            iframe.src = url;\n",
              "            iframe.setAttribute('width', '100%');\n",
              "            iframe.setAttribute('height', '800');\n",
              "            iframe.setAttribute('frameborder', 0);\n",
              "            document.body.appendChild(iframe);\n",
              "        })();\n",
              "    "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check which model is running on ollama"
      ],
      "metadata": {
        "id": "CNKGUkBuoHBL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!ollama ps"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C9lUle47oJqc",
        "outputId": "119d2496-cdef-4389-996d-ffa87344e32c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NAME           ID              SIZE      PROCESSOR    UNTIL            \n",
            "llama3.2:3b    a80c4f17acd5    4.0 GB    100% GPU     2 hours from now    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pypdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "kCX4mWRroMcb",
        "outputId": "1a3b3658-9e13-4633-c133-d09cd424314b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pypdf\n",
            "  Downloading pypdf-5.3.0-py3-none-any.whl.metadata (7.2 kB)\n",
            "Downloading pypdf-5.3.0-py3-none-any.whl (300 kB)\n",
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/300.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m300.7/300.7 kB\u001b[0m \u001b[31m19.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pypdf\n",
            "Successfully installed pypdf-5.3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Start the llama stack server"
      ],
      "metadata": {
        "id": "R51gr3T2oPMH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from google.colab import userdata\n",
        "\n",
        "os.environ['OPENAI_API_KEY'] = userdata.get('OPENAI_API_KEY')\n",
        "\n",
        "from llama_stack.distribution.library_client import LlamaStackAsLibraryClient\n",
        "client = LlamaStackAsLibraryClient(\"experimental-post-training\")\n",
        "_ = client.initialize()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "collapsed": true,
        "id": "nPrLIcC9oQB5",
        "outputId": "4f6d402c-82fc-4a9c-d589-98e3c324a84c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning: `bwrap` is not available. Code interpreter tool will not work correctly.\n",
            "INFO:datasets:PyTorch version 2.5.1+cu124 available.\n",
            "INFO:datasets:Polars version 1.9.0 available.\n",
            "INFO:datasets:Duckdb version 1.1.3 available.\n",
            "INFO:datasets:TensorFlow version 2.18.0 available.\n",
            "INFO:datasets:JAX version 0.4.33 available.\n",
            "INFO:llama_stack.distribution.stack:Scoring_fns: basic::equality served by basic\n",
            "INFO:llama_stack.distribution.stack:Scoring_fns: basic::subset_of served by basic\n",
            "INFO:llama_stack.distribution.stack:Scoring_fns: basic::regex_parser_multiple_choice_answer served by basic\n",
            "INFO:llama_stack.distribution.stack:Scoring_fns: braintrust::factuality served by braintrust\n",
            "INFO:llama_stack.distribution.stack:Scoring_fns: braintrust::answer-correctness served by braintrust\n",
            "INFO:llama_stack.distribution.stack:Scoring_fns: braintrust::answer-relevancy served by braintrust\n",
            "INFO:llama_stack.distribution.stack:Scoring_fns: braintrust::answer-similarity served by braintrust\n",
            "INFO:llama_stack.distribution.stack:Scoring_fns: braintrust::faithfulness served by braintrust\n",
            "INFO:llama_stack.distribution.stack:Scoring_fns: braintrust::context-entity-recall served by braintrust\n",
            "INFO:llama_stack.distribution.stack:Scoring_fns: braintrust::context-precision served by braintrust\n",
            "INFO:llama_stack.distribution.stack:Scoring_fns: braintrust::context-recall served by braintrust\n",
            "INFO:llama_stack.distribution.stack:Scoring_fns: braintrust::context-relevancy served by braintrust\n",
            "INFO:llama_stack.distribution.stack:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Using config \u001b[34mexperimental-post-training\u001b[0m:\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Using config <span style=\"color: #000080; text-decoration-color: #000080\">experimental-post-training</span>:\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "apis:\n",
              "- agents\n",
              "- datasetio\n",
              "- eval\n",
              "- inference\n",
              "- vector_io\n",
              "- safety\n",
              "- scoring\n",
              "- telemetry\n",
              "- post_training\n",
              "- tool_runtime\n",
              "benchmarks: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
              "container_image: null\n",
              "datasets: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
              "image_name: experimental-post-training\n",
              "metadata_store:\n",
              "  db_path: \u001b[35m/root/.llama/distributions/meta-reference-gpu/\u001b[0m\u001b[95mregistry.db\u001b[0m\n",
              "  namespace: null\n",
              "  type: sqlite\n",
              "models: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
              "providers:\n",
              "  agents:\n",
              "  - config:\n",
              "      persistence_store:\n",
              "        db_path: \u001b[35m/root/.llama/distributions/meta-reference-gpu/\u001b[0m\u001b[95magents_store.db\u001b[0m\n",
              "        namespace: null\n",
              "        type: sqlite\n",
              "    provider_id: meta-reference\n",
              "    provider_type: inline::meta-reference\n",
              "  datasetio:\n",
              "  - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
              "    provider_id: localfs\n",
              "    provider_type: inline::localfs\n",
              "  eval:\n",
              "  - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
              "    provider_id: meta-reference\n",
              "    provider_type: inline::meta-reference\n",
              "  inference:\n",
              "  - config:\n",
              "      checkpoint_dir: null\n",
              "      create_distributed_process_group: false\n",
              "      max_seq_len: \u001b[1;36m4096\u001b[0m\n",
              "    provider_id: meta-reference-inference\n",
              "    provider_type: inline::meta-reference\n",
              "  - config:\n",
              "      url: \u001b[4;94mhttp://localhost:11434\u001b[0m\n",
              "    provider_id: ollama\n",
              "    provider_type: remote::ollama\n",
              "  post_training:\n",
              "  - config:\n",
              "      checkpoint_format: huggingface\n",
              "    provider_id: torchtune-post-training\n",
              "    provider_type: inline::torchtune\n",
              "  safety:\n",
              "  - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
              "    provider_id: llama-guard\n",
              "    provider_type: inline::llama-guard\n",
              "  scoring:\n",
              "  - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
              "    provider_id: basic\n",
              "    provider_type: inlin\u001b[1;92me::ba\u001b[0msic\n",
              "  - config:\n",
              "      openai_api_key: \u001b[32m'********'\u001b[0m\n",
              "    provider_id: braintrust\n",
              "    provider_type: inlin\u001b[1;92me::b\u001b[0mraintrust\n",
              "  telemetry:\n",
              "  - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
              "    provider_id: meta-reference\n",
              "    provider_type: inline::meta-reference\n",
              "  tool_runtime:\n",
              "  - config:\n",
              "      api_key: \u001b[32m'********'\u001b[0m\n",
              "      max_results: \u001b[1;36m3\u001b[0m\n",
              "    provider_id: brave-search\n",
              "    provider_type: remot\u001b[1;92me::b\u001b[0mrave-search\n",
              "  vector_io:\n",
              "  - config:\n",
              "      kvstore:\n",
              "        db_path: \u001b[35m/root/.llama/distributions/meta-reference-gpu/\u001b[0m\u001b[95mfaiss_store.db\u001b[0m\n",
              "        namespace: null\n",
              "        type: sqlite\n",
              "    provider_id: faiss\n",
              "    provider_type: inlin\u001b[1;92me::fa\u001b[0miss\n",
              "scoring_fns: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
              "server:\n",
              "  port: \u001b[1;36m8321\u001b[0m\n",
              "  tls_certfile: null\n",
              "  tls_keyfile: null\n",
              "shields: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
              "tool_groups: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
              "vector_dbs: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
              "version: \u001b[32m'2'\u001b[0m\n",
              "\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">apis:\n",
              "- agents\n",
              "- datasetio\n",
              "- eval\n",
              "- inference\n",
              "- vector_io\n",
              "- safety\n",
              "- scoring\n",
              "- telemetry\n",
              "- post_training\n",
              "- tool_runtime\n",
              "benchmarks: <span style=\"font-weight: bold\">[]</span>\n",
              "container_image: null\n",
              "datasets: <span style=\"font-weight: bold\">[]</span>\n",
              "image_name: experimental-post-training\n",
              "metadata_store:\n",
              "  db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/root/.llama/distributions/meta-reference-gpu/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">registry.db</span>\n",
              "  namespace: null\n",
              "  type: sqlite\n",
              "models: <span style=\"font-weight: bold\">[]</span>\n",
              "providers:\n",
              "  agents:\n",
              "  - config:\n",
              "      persistence_store:\n",
              "        db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/root/.llama/distributions/meta-reference-gpu/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">agents_store.db</span>\n",
              "        namespace: null\n",
              "        type: sqlite\n",
              "    provider_id: meta-reference\n",
              "    provider_type: inline::meta-reference\n",
              "  datasetio:\n",
              "  - config: <span style=\"font-weight: bold\">{}</span>\n",
              "    provider_id: localfs\n",
              "    provider_type: inline::localfs\n",
              "  eval:\n",
              "  - config: <span style=\"font-weight: bold\">{}</span>\n",
              "    provider_id: meta-reference\n",
              "    provider_type: inline::meta-reference\n",
              "  inference:\n",
              "  - config:\n",
              "      checkpoint_dir: null\n",
              "      create_distributed_process_group: false\n",
              "      max_seq_len: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4096</span>\n",
              "    provider_id: meta-reference-inference\n",
              "    provider_type: inline::meta-reference\n",
              "  - config:\n",
              "      url: <span style=\"color: #0000ff; text-decoration-color: #0000ff; text-decoration: underline\">http://localhost:11434</span>\n",
              "    provider_id: ollama\n",
              "    provider_type: remote::ollama\n",
              "  post_training:\n",
              "  - config:\n",
              "      checkpoint_format: huggingface\n",
              "    provider_id: torchtune-post-training\n",
              "    provider_type: inline::torchtune\n",
              "  safety:\n",
              "  - config: <span style=\"font-weight: bold\">{}</span>\n",
              "    provider_id: llama-guard\n",
              "    provider_type: inline::llama-guard\n",
              "  scoring:\n",
              "  - config: <span style=\"font-weight: bold\">{}</span>\n",
              "    provider_id: basic\n",
              "    provider_type: inlin<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">e::ba</span>sic\n",
              "  - config:\n",
              "      openai_api_key: <span style=\"color: #008000; text-decoration-color: #008000\">'********'</span>\n",
              "    provider_id: braintrust\n",
              "    provider_type: inlin<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">e::b</span>raintrust\n",
              "  telemetry:\n",
              "  - config: <span style=\"font-weight: bold\">{}</span>\n",
              "    provider_id: meta-reference\n",
              "    provider_type: inline::meta-reference\n",
              "  tool_runtime:\n",
              "  - config:\n",
              "      api_key: <span style=\"color: #008000; text-decoration-color: #008000\">'********'</span>\n",
              "      max_results: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>\n",
              "    provider_id: brave-search\n",
              "    provider_type: remot<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">e::b</span>rave-search\n",
              "  vector_io:\n",
              "  - config:\n",
              "      kvstore:\n",
              "        db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/root/.llama/distributions/meta-reference-gpu/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">faiss_store.db</span>\n",
              "        namespace: null\n",
              "        type: sqlite\n",
              "    provider_id: faiss\n",
              "    provider_type: inlin<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">e::fa</span>iss\n",
              "scoring_fns: <span style=\"font-weight: bold\">[]</span>\n",
              "server:\n",
              "  port: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8321</span>\n",
              "  tls_certfile: null\n",
              "  tls_keyfile: null\n",
              "shields: <span style=\"font-weight: bold\">[]</span>\n",
              "tool_groups: <span style=\"font-weight: bold\">[]</span>\n",
              "vector_dbs: <span style=\"font-weight: bold\">[]</span>\n",
              "version: <span style=\"color: #008000; text-decoration-color: #008000\">'2'</span>\n",
              "\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Eval the native Llama model\n",
        "First of all, we'd like to measure the native Llama 3.2 3B instruct model performance as a tax preparer.\n",
        "\n",
        "#### 1.0. Prepare the eval dataset\n",
        "\n",
        "We prepared a synthetic tax Q&A dataset from Llama 3.3 70B model [tax_preparation_eval.csv](https://gist.github.com/SLR722/0420c558ec681b00ed05fa1171505a38) (data source: https://github.com/shadi-fsai/modeluniversity/blob/main/test_questions.json).\n",
        "\n",
        "- You can create your own eval dataset that repects Llama stack [eval dataset format](https://github.com/meta-llama/llama-stack/blob/91907b714e825a1bfbca5271e0f403aab5f10752/llama_stack/providers/utils/common/data_schema_validator.py#L43)\n",
        "\n"
      ],
      "metadata": {
        "id": "EpdByzupodfF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "\n",
        "# Upload the example dataset from github to notebook\n",
        "url = 'https://gist.githubusercontent.com/SLR722/0420c558ec681b00ed05fa1171505a38/raw/dbc7ab86e71e808c4bae50b68b8bff60c1d239a5/tax_preparation_eval.csv'\n",
        "r = requests.get(url)\n",
        "with open('tax_preparation_eval.csv', 'wb') as f:\n",
        "    f.write(r.content)\n",
        "\n",
        "# You can use the below comment out code to upload your local file to the notebook\n",
        "# from google.colab import files\n",
        "\n",
        "# uploaded = files.upload()"
      ],
      "metadata": {
        "id": "5nIlFkvBHP0n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import mimetypes\n",
        "import base64\n",
        "\n",
        "# encode the dataset file into data_url\n",
        "def data_url_from_file(file_path: str) -> str:\n",
        "    if not os.path.exists(file_path):\n",
        "        raise FileNotFoundError(f\"File not found: {file_path}\")\n",
        "\n",
        "    with open(file_path, \"rb\") as file:\n",
        "        file_content = file.read()\n",
        "\n",
        "    base64_content = base64.b64encode(file_content).decode(\"utf-8\")\n",
        "    mime_type, _ = mimetypes.guess_type(file_path)\n",
        "\n",
        "    data_url = f\"data:{mime_type};base64,{base64_content}\"\n",
        "\n",
        "    return data_url\n",
        "\n",
        "data_url = data_url_from_file(\"tax_preparation_eval.csv\")\n",
        "\n",
        "# register the eval dataset\n",
        "response = client.datasets.register(\n",
        "    dataset_id=\"eval_dataset\",\n",
        "    provider_id=\"localfs\",\n",
        "    url={\"uri\": data_url},\n",
        "    dataset_schema={\n",
        "        \"input_query\": {\"type\": \"string\"},\n",
        "        \"chat_completion_input\": {\"type\": \"chat_completion_input\"},\n",
        "        \"expected_answer\": {\"type\": \"string\"},\n",
        "    },\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vwKcfZc89sNf",
        "outputId": "b2c98321-1a25-46ff-e82a-7522e068f9c0",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2m00:30:00.325\u001b[0m \u001b[35m[START]\u001b[0m \u001b[2m/v1/datasets\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1.1. Register the eval model candidate with [models APIs](https://github.com/meta-llama/llama-stack/blob/e3f187fb83f2c45d5f838663658a873fb0fcc6d9/llama_stack/apis/models/models.py)\n",
        "Since we use ollama as provider for inference, we set provider_id to 'ollama' during model registration\n"
      ],
      "metadata": {
        "id": "KF_nxqEZ-FQa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from rich.pretty import pprint\n",
        "\n",
        "response = client.models.register(\n",
        "    model_id=\"meta-llama/Llama-3.2-3B-Instruct\",\n",
        "    provider_id=\"ollama\",\n",
        "    provider_model_id=\"llama3.2:3b\",\n",
        "    # base model id\n",
        "    metadata={\"llama_model\": \"meta-llama/Llama-3.2-3B-Instruct\"},\n",
        ")\n",
        "\n",
        "pprint(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "id": "Le1WDhlg-ys5",
        "outputId": "6ee3f9b6-ceda-4653-82c5-879c636027c6",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:httpx:HTTP Request: GET http://localhost:11434/api/ps \"HTTP/1.1 200 OK\"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2m00:30:29.540\u001b[0m \u001b[35m[START]\u001b[0m \u001b[2m/v1/models\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1;35mModel\u001b[0m\u001b[1m(\u001b[0m\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33midentifier\u001b[0m=\u001b[32m'meta-llama/Llama-3.2-3B-Instruct'\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'llama_model'\u001b[0m: \u001b[32m'meta-llama/Llama-3.2-3B-Instruct'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mapi_model_type\u001b[0m=\u001b[32m'llm'\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mprovider_id\u001b[0m=\u001b[32m'ollama'\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mprovider_resource_id\u001b[0m=\u001b[32m'llama3.2:3b'\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'model'\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mmodel_type\u001b[0m=\u001b[32m'llm'\u001b[0m\n",
              "\u001b[1m)\u001b[0m\n"
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            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Model</span><span style=\"font-weight: bold\">(</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">identifier</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'meta-llama/Llama-3.2-3B-Instruct'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">metadata</span>=<span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'llama_model'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'meta-llama/Llama-3.2-3B-Instruct'</span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">api_model_type</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'llm'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">provider_id</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'ollama'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">provider_resource_id</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'llama3.2:3b'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">type</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'model'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">model_type</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'llm'</span>\n",
              "<span style=\"font-weight: bold\">)</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1.2. Kick-off eval job\n",
        "- More details on Llama-stack eval: https://llama-stack.readthedocs.io/en/latest/benchmark_evaluations/index.html\n",
        "  - Define an EvalCandidate\n",
        "  - Run evaluate on datasets (we choose brainstrust's answer-similarity as scoring function with OpenAI's model as judge model)\n",
        "\n",
        "  > **Note**: If the eval process is stuck, try to restart the ollama server and try again\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "nwT5PPJs_TU9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eval_rows = client.datasetio.get_rows_paginated(\n",
        "    dataset_id=\"eval_dataset\",\n",
        "    rows_in_page=-1,\n",
        ")\n",
        "\n",
        "from tqdm import tqdm\n",
        "\n",
        "client.benchmarks.register(\n",
        "    benchmark_id=\"llama3.2-3B-instruct:tax_eval\",\n",
        "    dataset_id=\"eval_dataset\",\n",
        "    scoring_functions=[\"braintrust::answer-similarity\"]\n",
        ")\n",
        "\n",
        "response = client.eval.evaluate_rows(\n",
        "    benchmark_id=\"llama3.2-3B-instruct:tax_eval\",\n",
        "    input_rows=eval_rows.rows,\n",
        "    scoring_functions=[\"braintrust::answer-similarity\"],\n",
        "    benchmark_config={\n",
        "        \"type\": \"benchmark\",\n",
        "        \"eval_candidate\": {\n",
        "            \"type\": \"model\",\n",
        "            \"model\": \"meta-llama/Llama-3.2-3B-Instruct\",\n",
        "            \"sampling_params\": {\n",
        "                \"temperature\": 0.0,\n",
        "                \"max_tokens\": 4096,\n",
        "                \"top_p\": 0.9,\n",
        "                \"repeat_penalty\": 1.0,\n",
        "            },\n",
        "        }\n",
        "    }\n",
        ")\n",
        "pprint(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "A1VJC5dJ_7n9",
        "outputId": "f628b73c-f1e5-4456-a153-3176601902b7",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2m00:35:56.357\u001b[0m \u001b[35m[START]\u001b[0m \u001b[2m/v1/datasetio/rows\u001b[0m\n",
            "\u001b[2m00:35:56.357\u001b[0m \u001b[35m[END]\u001b[0m \u001b[2m/v1/datasetio/rows\u001b[0m\u001b[0m [StatusCode.OK]\u001b[0m (0.31ms)\n",
            "\u001b[2m00:35:56.369\u001b[0m \u001b[35m[START]\u001b[0m \u001b[2m/v1/eval/benchmarks\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 0/43 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2m00:35:56.378\u001b[0m \u001b[35m[END]\u001b[0m \u001b[2m/v1/eval/benchmarks\u001b[0m\u001b[0m [StatusCode.OK]\u001b[0m (8.48ms)\n",
            "\u001b[2m00:35:56.397\u001b[0m \u001b[35m[START]\u001b[0m \u001b[2m/v1/eval/benchmarks/llama3.2-3B-instruct:tax_eval/evaluations\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:httpx:HTTP Request: POST http://localhost:11434/api/generate \"HTTP/1.1 200 OK\"\n",
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            "100%|██████████| 43/43 [02:37<00:00,  3.65s/it]\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
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              "\u001b[1;35mEvaluateResponse\u001b[0m\u001b[1m(\u001b[0m\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mgenerations\u001b[0m=\u001b[1m[\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"The primary purpose of a W-2 form, also known as a Wage and Tax Statement, is to report an employee's income earned from their employer to the Internal Revenue Service \u001b[0m\u001b[32m(\u001b[0m\u001b[32mIRS\u001b[0m\u001b[32m)\u001b[0m\u001b[32m for federal income tax purposes. The W-2 form is used by employers to provide employees with a summary of their earnings and taxes withheld from their paychecks throughout the year.\\n\\nThe W-2 form typically includes information such as:\\n\\n* Employee's name, address, and Social Security number\\n* Employer's name, address, and Employer Identification Number \u001b[0m\u001b[32m(\u001b[0m\u001b[32mEIN\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* Gross wages earned during the tax year\\n* Federal income tax withheld\\n* State and local taxes withheld \u001b[0m\u001b[32m(\u001b[0m\u001b[32mif applicable\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* Other deductions and credits claimed by the employee\\n\\nThe primary purpose of a W-2 form is to:\\n\\n1. Report an employee's income to the IRS: The W-2 form serves as proof of income earned by employees, which is used by the IRS to determine how much tax should be withheld from future paychecks.\\n2. Provide information for tax withholding: The W-2 form helps employers calculate and withhold the correct amount of federal income tax, Social Security tax, and Medicare tax from an employee's wages.\\n3. Allow employees to file their tax returns accurately: By providing a summary of their earnings and taxes withheld, the W-2 form enables employees to complete their tax returns accurately and claim any additional credits or deductions they may be eligible for.\\n\\nOverall, the W-2 form plays a critical role in ensuring that employers comply with federal income tax laws and regulations, while also helping employees manage their tax obligations and take advantage of available credits and deductions.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I'd be happy to explain how W-2 income affects an individual's tax liability.\\n\\nW-2 income refers to the wages and salaries earned by an employee from their employer. The amount of W-2 income reported on an individual's W-2 form is used to determine their taxable income for the year. Here are some ways in which W-2 income can affect an individual's tax liability:\\n\\n1. **Taxable Income**: W-2 income is considered taxable income, meaning it is subject to federal and state income taxes. The amount of W-2 income reported on the form will be used to calculate the individual's total taxable income for the year.\\n2. **Tax Brackets**: W-2 income falls into one of several tax brackets, which determine the tax rate applied to that income. As an individual earns more W-2 income, they may move up or down a tax bracket, affecting their overall tax liability.\\n3. **Deductions and Credits**: Depending on the individual's circumstances, they may be eligible for deductions and credits that can reduce their taxable income. For example, if an individual has health insurance premiums through their employer, they may be able to claim a deduction for those premiums. Similarly, if they have children or are married, they may be eligible for credits like the Earned Income Tax Credit \u001b[0m\u001b[32m(\u001b[0m\u001b[32mEITC\u001b[0m\u001b[32m)\u001b[0m\u001b[32m or Child Tax Credit.\\n4. **Self-Employment Taxes**: If an individual has self-employment income reported on their W-2 form, it will also be subject to self-employment taxes. This can increase their overall tax liability, as self-employment taxes are typically 15.3% of net earnings from self-employment \u001b[0m\u001b[32m(\u001b[0m\u001b[32m12.4% for Social Security and 2.9% for Medicare\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n5. **Tax Credits**: Some W-2 income may qualify for tax credits, such as the Child Tax Credit or Education Credits. These credits can directly reduce an individual's tax liability.\\n6. **Tax Withholding**: Employers are required to withhold federal income taxes from employee wages and pay them over to the government on behalf of their employees. This withholding reduces the amount of taxes owed when filing a tax return, but it also means that some taxes may be withheld too much, resulting in a larger refund or less tax liability.\\n7. **Tax Liabilities**: If an individual has W-2 income from multiple sources \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., employment and self-employment\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, their overall tax liability will depend on the combined amount of taxable income.\\n\\nTo minimize tax liability, individuals with W-2 income should consider factors such as:\\n\\n* Taking advantage of deductions and credits available to them\\n* Adjusting withholding amounts through payroll adjustments or estimated tax payments\\n* Considering retirement savings options, like 401\u001b[0m\u001b[32m(\u001b[0m\u001b[32mk\u001b[0m\u001b[32m)\u001b[0m\u001b[32m or IRA contributions\\n* Reviewing their overall financial situation to identify areas for tax optimization\\n\\nAs a tax preparer, I would work with clients to help them navigate these factors and optimize their W-2 income to minimize their tax liability.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I can tell you that W-2 income is generally considered taxable income and cannot be adjusted for tax purposes.\\n\\nA W-2 form is used to report an employee's wages and taxes withheld from their paycheck. The income reported on the W-2 is considered taxable income and must be included in the taxpayer's gross income for tax purposes.\\n\\nHowever, there are some exceptions and potential adjustments that can be made to W-2 income for tax purposes:\\n\\n1. **Corrected W-2s**: If an employer makes a mistake on the W-2 form, such as underreporting or overpaying taxes withheld, they may issue a corrected W-2 to the employee. In this case, the corrected amount can be adjusted on the taxpayer's return.\\n2. **Tax credits and deductions**: Taxpayers may be eligible for tax credits or deductions that reduce their taxable income, such as the Earned Income Tax Credit \u001b[0m\u001b[32m(\u001b[0m\u001b[32mEITC\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, Child Tax Credit, or education credits. These credits and deductions can reduce the amount of W-2 income subject to taxation.\\n3. **Self-employment income**: If an employee has self-employment income reported on a 1099-MISC form, they may be able to deduct business expenses related to that income on their tax return. This can potentially reduce their taxable income from the W-2 income.\\n4. **Tax law changes**: Changes in tax laws or regulations can affect how W-2 income is taxed. For example, if a new tax law reduces the tax rate for certain types of income, it may be possible to adjust the taxpayer's return to reflect this change.\\n\\nHowever, these exceptions and adjustments are subject to specific rules and requirements, and taxpayers should consult with a tax professional or the IRS to determine the best course of action.\\n\\nIn general, W-2 income is considered taxable income and cannot be adjusted for tax purposes without proper documentation and approval from the employer or the IRS.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I can tell you that the Internal Revenue Service \u001b[0m\u001b[32m(\u001b[0m\u001b[32mIRS\u001b[0m\u001b[32m)\u001b[0m\u001b[32m uses various methods to verify W-2 income. Here are some of the ways they verify W-2 income:\\n\\n1. **Employer Reporting**: The most common method is through employer reporting. Employers are required to provide employees with a Form W-2, Wage and Tax Statement, by January 31st of each year, showing their wages, taxes withheld, and other relevant information. This form serves as proof of employment income.\\n2. **Form 1099-MISC**: If an individual receives freelance or contract work, they may receive a Form 1099-MISC, Miscellaneous Income, from the payer. This form reports non-employee compensation, such as freelance work, rent, and royalties.\\n3. **Bank Statements**: The IRS can review bank statements to verify income reported on W-2s. They may request bank statements to confirm that the income reported on the W-2 is accurate.\\n4. **Employer Verification Letters**: In some cases, the IRS may request a letter from the employer verifying the employee's income and employment status.\\n5. **Taxpayer Identification Number \u001b[0m\u001b[32m(\u001b[0m\u001b[32mTIN\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Verification**: The IRS can verify an individual's TIN through various sources, including:\\n\\t* Social Security Administration \u001b[0m\u001b[32m(\u001b[0m\u001b[32mSSA\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* Internal Revenue Service \u001b[0m\u001b[32m(\u001b[0m\u001b[32mIRS\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* State tax agencies\\n\\t* Other government agencies\\n6. **Address Verification**: The IRS may request verification of an individual's address to ensure that the W-2 is being sent to the correct address.\\n7. **Audit Trails**: Employers are required to maintain records of employee wages and taxes withheld for at least three years. These records can be reviewed by the IRS during an audit.\\n\\nTo verify W-2 income, the IRS may use various tools and resources, including:\\n\\n1. The Electronic Federal Tax Payment System \u001b[0m\u001b[32m(\u001b[0m\u001b[32mEFTPS\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n2. The IRS Data Retrieval Tool\\n3. The IRS's online database of tax returns and transcripts\\n\\nIt's worth noting that the IRS can request additional documentation or information to verify W-2 income if they suspect any discrepancies or errors on the return. As a tax preparer, it's essential to ensure that all required documentation is accurate and complete to avoid any potential issues with the IRS.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I'd be happy to explain how pre-tax deductions can impact W-2 income.\\n\\nPre-tax deductions, also known as pre-tax contributions or pre-tax withholdings, refer to amounts withheld from an employee's paycheck before taxes are taken out. These deductions are typically made through payroll deductions, such as 401\u001b[0m\u001b[32m(\u001b[0m\u001b[32mk\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, 403\u001b[0m\u001b[32m(\u001b[0m\u001b[32mb\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, Health Savings Account \u001b[0m\u001b[32m(\u001b[0m\u001b[32mHSA\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, Flexible Spending Arrangements \u001b[0m\u001b[32m(\u001b[0m\u001b[32mFSA\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, or other qualified retirement plans.\\n\\nWhen it comes to W-2 income, pre-tax deductions can affect the amount of taxable income reported on your tax return. Here's how:\\n\\n1. **Reduced Gross Income**: Pre-tax deductions are subtracted from your gross income before taxes are taken out. This means that the amount of money you take home each paycheck is lower than your gross income.\\n2. **Lower Taxable Income**: Since pre-tax deductions reduce your gross income, they also reduce your taxable income. As a result, your tax liability will be lower, and you may receive a larger refund or pay less in taxes throughout the year.\\n3. **Tax-Deferred Growth**: Pre-tax contributions to retirement plans like 401\u001b[0m\u001b[32m(\u001b[0m\u001b[32mk\u001b[0m\u001b[32m)\u001b[0m\u001b[32m or 403\u001b[0m\u001b[32m(\u001b[0m\u001b[32mb\u001b[0m\u001b[32m)\u001b[0m\u001b[32m grow tax-deferred, meaning they are not subject to income tax until withdrawal. This can help your savings grow faster over time.\\n\\nTo illustrate this concept, let's consider an example:\\n\\nSuppose you earn $50,000 per year and contribute $5,000 to a 401\u001b[0m\u001b[32m(\u001b[0m\u001b[32mk\u001b[0m\u001b[32m)\u001b[0m\u001b[32m plan through payroll deductions. Your gross income would be reduced by $5,000, making your take-home pay $45,000. Since the contribution is made before taxes are taken out, it reduces your taxable income.\\n\\nOn your tax return, you'll report your adjusted gross income \u001b[0m\u001b[32m(\u001b[0m\u001b[32mAGI\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, which includes the pre-tax contributions to your 401\u001b[0m\u001b[32m(\u001b[0m\u001b[32mk\u001b[0m\u001b[32m)\u001b[0m\u001b[32m plan. This can result in a lower AGI and potentially lower taxes owed or a larger refund.\\n\\nKeep in mind that while pre-tax deductions reduce your taxable income, they also reduce your take-home pay. It's essential to consider how these deductions impact your overall financial situation and adjust your budget accordingly.\\n\\nAs a tax preparer, I always advise clients to review their W-2 income and pre-tax deductions to ensure they're taking advantage of available tax savings opportunities while maintaining a healthy balance between saving for retirement and enjoying their hard-earned money.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I can tell you that yes, it is possible for an individual to receive W-2 income from multiple employers and have those amounts reported on separate W-2 forms.\\n\\nIn general, the IRS requires each employer to report all wages, tips, and other compensation paid to an employee on a single W-2 form. However, there are some exceptions and special circumstances that may result in multiple W-2 forms being issued:\\n\\n1. **Multiple jobs**: If you have multiple jobs or positions with different employers during the same tax year, each employer will issue a separate W-2 form showing their portion of your total income.\\n2. **Self-employment income**: If you are self-employed and earn income from a business or freelance work, you may receive a 1099-MISC form \u001b[0m\u001b[32m(\u001b[0m\u001b[32mnot a W-2\u001b[0m\u001b[32m)\u001b[0m\u001b[32m from yourself as the business owner. However, if you also have other employment income reported on a W-2, both forms will be issued.\\n3. **Gig economy workers**: If you work through platforms like Uber, Lyft, or Airbnb, you may receive multiple 1099-K forms \u001b[0m\u001b[32m(\u001b[0m\u001b[32mnot W-2s\u001b[0m\u001b[32m)\u001b[0m\u001b[32m from these companies, as they are considered independent contractors rather than employees.\\n4. **Government employment**: Federal, state, and local government employees typically receive a single W-2 form showing their total compensation for the year.\\n5. **Retirement plan distributions**: If you receive retirement plan distributions \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., 401\u001b[0m\u001b[32m(\u001b[0m\u001b[32mk\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, IRA\u001b[0m\u001b[32m)\u001b[0m\u001b[32m from multiple sources, each plan may issue separate W-2 forms or 1099-R forms.\\n\\nWhen an individual receives income from multiple sources, it's essential to report all of these amounts on their tax return. The IRS requires that you combine the income from all sources and report it on your tax return, regardless of whether it was reported on a single W-2 form or multiple ones.\\n\\nAs a tax preparer, I would ensure that my clients accurately report all income from multiple sources on their tax returns to avoid any potential issues with the IRS.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I'd be happy to explain how W-2 income is affected by tax credits.\\n\\nW-2 income refers to the wages and salaries reported on your Form W-2, which you receive from your employer at the end of each year. Tax credits are deductions or reductions in the amount of taxes you owe, rather than a direct reduction in your taxable income.\\n\\nHere's how W-2 income is affected by tax credits:\\n\\n1. **Taxable income**: Your W-2 income is considered taxable income and is subject to federal income tax withholding.\\n2. **Tax credits vs. deductions**: Tax credits are different from deductions. Deductions reduce the amount of income that is subject to taxation, while credits directly reduce the amount of taxes you owe.\\n3. **Tax credits can reduce or eliminate taxes owed**: If you have eligible tax credits, such as the Earned Income Tax Credit \u001b[0m\u001b[32m(\u001b[0m\u001b[32mEITC\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, Child Tax Credit, or Education Credits, these credits can reduce your taxable income and, in some cases, even result in a refund if the credit exceeds the amount of taxes owed.\\n4. **Tax credits may not directly affect W-2 income**: However, tax credits can indirectly affect your W-2 income by reducing the amount of taxes withheld from your paycheck. For example, if you have a child and are eligible for the Child Tax Credit, your employer may reduce the amount of federal income tax withheld from your paychecks to reflect the credit.\\n5. **Tax credits can increase your refund**: If you have multiple tax credits that exceed your tax liability, you may receive a larger refund than you would if you didn't have any credits.\\n\\nTo illustrate this, let's consider an example:\\n\\nSuppose John has W-2 income of $50,000 and is eligible for the Earned Income Tax Credit \u001b[0m\u001b[32m(\u001b[0m\u001b[32mEITC\u001b[0m\u001b[32m)\u001b[0m\u001b[32m worth $5,000. His total tax liability before credits would be approximately 20% of his taxable income \u001b[0m\u001b[32m(\u001b[0m\u001b[32m$10,000\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. With the EITC credit, his new tax liability would be reduced to $5,000, resulting in a larger refund.\\n\\nIn summary, W-2 income is subject to taxation and withholding, but tax credits can reduce your taxable income or directly reduce the amount of taxes owed. Tax credits can also indirectly affect your W-2 income by reducing the amount of taxes withheld from your paycheck.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'As a tax preparer, I\\'d be happy to explain how W-2 income affects the Alternative Minimum Tax \u001b[0m\u001b[32m(\u001b[0m\u001b[32mAMT\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n\\nThe Alternative Minimum Tax \u001b[0m\u001b[32m(\u001b[0m\u001b[32mAMT\u001b[0m\u001b[32m)\u001b[0m\u001b[32m is a provision in the US tax code that requires individuals and businesses to pay taxes at a minimum rate of 26% on certain types of income. The AMT was created to ensure that taxpayers don\\'t benefit from tax loopholes and deductions that allow them to avoid paying their \"fair share\" of taxes.\\n\\nW-2 income, which represents the income earned by employees, is subject to the AMT if it exceeds certain thresholds. Here\\'s how W-2 income affects the AMT:\\n\\n1. **AMT Exclusion**: The first $80,250 of W-2 income \u001b[0m\u001b[32m(\u001b[0m\u001b[32mfor tax year 2022\u001b[0m\u001b[32m)\u001b[0m\u001b[32m is exempt from the AMT. This means that if your W-2 income is below this threshold, you won\\'t be subject to the AMT.\\n2. **AMT Taxable Income**: If your W-2 income exceeds the $80,250 threshold, it\\'s considered taxable income for AMT purposes. The amount above the threshold is then used to calculate the AMT liability.\\n3. **AMT Deductions and Credits**: Certain deductions and credits can reduce the AMT liability. These include:\\n\\t* Personal exemptions \u001b[0m\u001b[32m(\u001b[0m\u001b[32mstandard deduction or itemized deductions\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* State and local taxes\\n\\t* Mortgage interest and property taxes\\n\\t* Charitable contributions\\n\\t* Medical expenses\\n4. **AMT Exemptions**: Some types of income are exempt from the AMT, including:\\n\\t* Interest on certain types of bonds \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., municipal bonds\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* Dividend income from qualified dividends\\n\\t* Capital gains from investments\\n\\nTo determine if you\\'re subject to the AMT, your W-2 income is compared to the AMT exemption amount. If your W-2 income exceeds the exemption amount, you\\'ll need to complete Form 6251 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mAlternative Minimum Tax - Individual\u001b[0m\u001b[32m)\u001b[0m\u001b[32m and calculate your AMT liability.\\n\\nKeep in mind that the AMT can be complex, and there are many factors that can affect your eligibility for exemptions and deductions. As a tax preparer, I would work with you to ensure you\\'re taking advantage of all eligible deductions and credits to minimize your AMT liability.\\n\\nDo you have any specific questions about how W-2 income affects the AMT or would you like me to elaborate on any of these points?'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"The Tax Cuts and Jobs Act \u001b[0m\u001b[32m(\u001b[0m\u001b[32mTCJA\u001b[0m\u001b[32m)\u001b[0m\u001b[32m made significant changes to the way W-2 income is taxed, particularly for employees who receive a W-2 form from their employer. Here are some key ways in which the TCJA affects W-2 income:\\n\\n1. **Standard Deduction Increase**: The TCJA increased the standard deduction for single filers from $6,350 to $12,000 and for joint filers from $12,700 to $24,400. This means that more employees may not need to itemize their deductions on their tax return, which can reduce their W-2 income.\\n2. **State and Local Taxes \u001b[0m\u001b[32m(\u001b[0m\u001b[32mSALT\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Limitation**: The TCJA limited the deduction for state and local taxes \u001b[0m\u001b[32m(\u001b[0m\u001b[32mSALT\u001b[0m\u001b[32m)\u001b[0m\u001b[32m to $10,000 per year. This means that if an employee's SALT deduction exceeds $10,000, they may not be able to deduct it on their tax return.\\n3. **Child Tax Credit**: The TCJA increased the child tax credit from $1,000 to $2,000 per child under age 17 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mor $3,000 for one qualifying child under age 17 if both parents are claimed as dependents\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. This can result in a larger W-2 income for employees with children.\\n4. **Earned Income Tax Credit \u001b[0m\u001b[32m(\u001b[0m\u001b[32mEITC\u001b[0m\u001b[32m)\u001b[0m\u001b[32m**: The TCJA expanded the EITC to include more low-to-moderate-income workers, which may increase their W-2 income due to the increased credit amount.\\n5. **Health Savings Account \u001b[0m\u001b[32m(\u001b[0m\u001b[32mHSA\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Contributions**: The TCJA allowed employees to contribute up to $3,550 to a Health Savings Account \u001b[0m\u001b[32m(\u001b[0m\u001b[32mHSA\u001b[0m\u001b[32m)\u001b[0m\u001b[32m in 2019 and 2020, an increase from $3,300 in previous years. This can result in a larger W-2 income for employees who participate in an HSA.\\n6. **Retirement Plan Contributions**: The TCJA increased the annual contribution limits for 401\u001b[0m\u001b[32m(\u001b[0m\u001b[32mk\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, 403\u001b[0m\u001b[32m(\u001b[0m\u001b[32mb\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, and other retirement plans. This may result in a larger W-2 income for employees who contribute to these plans.\\n\\nHowever, it's essential to note that not all W-2 income is affected by the TCJA. For example:\\n\\n* **Self-Employment Income**: Self-employed individuals are not subject to the same tax changes as employees with W-2 income.\\n* **Health Insurance Premiums**: The TCJA did not change the way health insurance premiums are taxed, so this will not affect W-2 income.\\n\\nIt's always a good idea for employees to consult with their employer or a tax professional to understand how the TCJA affects their specific situation and to ensure they're taking advantage of any available tax savings opportunities.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"The Net Investment Income Tax \u001b[0m\u001b[32m(\u001b[0m\u001b[32mNIIT\u001b[0m\u001b[32m)\u001b[0m\u001b[32m is a provision in the Tax Cuts and Jobs Act \u001b[0m\u001b[32m(\u001b[0m\u001b[32mTCJA\u001b[0m\u001b[32m)\u001b[0m\u001b[32m that was enacted in 2017. It applies to certain types of investment income, including interest, dividends, capital gains, and qualified dividend income.\\n\\nW-2 income, on the other hand, is ordinary income earned from employment, such as wages, salaries, tips, and other forms of compensation received by an individual for their work.\\n\\nThe impact of W-2 income on the Net Investment Income Tax \u001b[0m\u001b[32m(\u001b[0m\u001b[32mNIIT\u001b[0m\u001b[32m)\u001b[0m\u001b[32m is that it does not directly affect the NIIT. The NIIT only applies to investment income, which includes:\\n\\n* Interest income from bonds, CDs, and other debt instruments\\n* Dividend income from stocks and mutual funds\\n* Capital gains from the sale of securities\\n* Qualified dividend income from certain types of investments\\n\\nW-2 income is considered ordinary income and is subject to regular income tax rates, not the NIIT. However, if you have investment income that is subject to the NIIT, your W-2 income may be used to offset some or all of the excess investment income.\\n\\nFor example, let's say you have a W-2 income of $50,000 and also have $20,000 in interest income from bonds. If your total taxable income exceeds the standard deduction amount for your filing status, you would pay tax on both the W-2 income and the interest income. However, if your investment income is subject to the NIIT, it may reduce your overall tax liability.\\n\\nTo illustrate this, let's say your W-2 income is $50,000 and your total taxable income is $60,000 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mafter deductions\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. If you have $20,000 in interest income that is subject to the NIIT, your effective tax rate on the investment income would be 3.8% \u001b[0m\u001b[32m(\u001b[0m\u001b[32mthe top marginal rate for single filers with modified adjusted gross income above $200,000 or $250,000 for joint filers\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. In this scenario, you would pay 3.8% of $20,000 in interest income, which is $760.\\n\\nIn contrast, your W-2 income would be taxed at the regular tax rates, which might be 24% \u001b[0m\u001b[32m(\u001b[0m\u001b[32mthe top marginal rate for single filers with taxable income above $80,000\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. In this scenario, you would pay 24% of $50,000 in W-2 income, which is $12,000.\\n\\nIn summary, while W-2 income does not directly impact the Net Investment Income Tax \u001b[0m\u001b[32m(\u001b[0m\u001b[32mNIIT\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, it can affect your overall tax liability if you have significant investment income that is subject to the NIIT.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I'd be happy to explain how W-2 income is affected by the Affordable Care Act \u001b[0m\u001b[32m(\u001b[0m\u001b[32mACA\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n\\nThe ACA, also known as Obamacare, has had a significant impact on W-2 income in several ways:\\n\\n1. **Health Insurance Premium Tax Credit**: The ACA introduced a premium tax credit for individuals and families who purchase health insurance through the Health Insurance Marketplace or their employer-sponsored plan. This credit can reduce the amount of taxes owed on W-2 income.\\n2. **Health Savings Account \u001b[0m\u001b[32m(\u001b[0m\u001b[32mHSA\u001b[0m\u001b[32m)\u001b[0m\u001b[32m contributions**: If you have a high-deductible health plan, you may be eligible to contribute to an HSA. Contributions to HSAs are tax-deductible and can be used for qualified medical expenses. The ACA has expanded the types of expenses that qualify for HSA funding.\\n3. **Dependent care credits**: The ACA introduced new dependent care credits for families with qualifying children under age 13 or disabled individuals who need care. These credits can reduce W-2 income subject to self-employment tax.\\n4. **Medicare taxes**: The ACA has changed the way Medicare taxes are applied to W-2 income. For employees, Medicare taxes are now split between the employee and employer, with the employer paying 1.45% of wages up to $200,000 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mpreviously $110,100\u001b[0m\u001b[32m)\u001b[0m\u001b[32m and 0.45% above that amount.\\n5. **Health insurance premiums**: The ACA has required employers to offer health insurance coverage to their employees or face penalties. This means that many W-2 income earners may have had health insurance coverage through their employer, which can impact their tax obligations.\\n\\nTo take advantage of these benefits, individuals and families must meet certain eligibility requirements, such as:\\n\\n* Being under age 65\\n* Not being eligible for Medicare\\n* Having a qualifying child or dependent\\n* Meeting income limits \u001b[0m\u001b[32m(\u001b[0m\u001b[32mvaries by family size and filing status\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\nAs a tax preparer, I would need to review each client's individual circumstances to determine how the ACA affects their W-2 income. This may involve reviewing their health insurance coverage, HSA contributions, dependent care credits, Medicare taxes, and other factors to ensure they are taking advantage of all eligible benefits.\\n\\nKeep in mind that tax laws and regulations can change frequently, so it's essential to stay informed about any updates or changes that may affect W-2 income.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'As a tax preparer, I\\'d be happy to explain how W-2 income affects self-employment tax.\\n\\nSelf-employment tax is a type of tax that is used to fund Social Security and Medicare. It\\'s typically paid by individuals who are self-employed or have a side hustle. The good news is that you don\\'t pay self-employment tax on your W-2 income, but there are some nuances to consider.\\n\\nHere\\'s the key point: if you receive a W-2 from an employer, you\\'re not subject to self-employment tax on that income because it\\'s considered \"earned income\" rather than self-employment income. Earned income is income earned through employment, such as wages or salaries.\\n\\nHowever, there are some exceptions and considerations:\\n\\n1. **Self-Employment Tax on Business Income**: If you have a side hustle or business, you\\'re subject to self-employment tax on the net earnings from that business. This includes income from freelancing, consulting, renting out a room on Airbnb, or any other type of business activity.\\n2. **Net Earnings from Self-Employment**: To calculate self-employment tax, you need to determine your net earnings from self-employment. This is calculated by subtracting business expenses and deductions from your gross income. If your net earnings are $400 or more, you\\'re subject to self-employment tax.\\n3. **Self-Employment Tax Rate**: The self-employment tax rate is 15.3% of your net earnings from self-employment, which includes both the employee and employer portions of Social Security and Medicare taxes. This rate applies to your net earnings from self-employment, not your W-2 income.\\n4. **Self-Employment Tax Deduction**: You can deduct half of your self-employment tax as a business expense on Schedule C \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. This can help reduce your taxable income and lower your overall tax liability.\\n\\nTo illustrate this, let\\'s say you have a side hustle that generates $50,000 in net earnings from self-employment. Your self-employment tax would be:\\n\\n$50,000 x 15.3% = $7,650\\n\\nYou can deduct half of this amount as a business expense on Schedule C, which reduces your taxable income and lowers your overall tax liability.\\n\\nIn summary, W-2 income is not subject to self-employment tax because it\\'s considered earned income, but if you have a side hustle or business, you\\'re subject to self-employment tax on the net earnings from that activity.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I'd be happy to explain how W-2 income is affected by the Foreign Earned Income Exclusion.\\n\\nThe Foreign Earned Income Exclusion \u001b[0m\u001b[32m(\u001b[0m\u001b[32mFEIE\u001b[0m\u001b[32m)\u001b[0m\u001b[32m is a tax benefit that allows certain individuals to exclude up to a certain amount of foreign-earned income from their U.S. taxable income. This exclusion can significantly reduce or even eliminate the amount of taxes owed on foreign-earned income, making it an attractive option for expats and international workers.\\n\\nHere's how W-2 income is affected by the FEIE:\\n\\n1. **Eligibility**: To qualify for the FEIE, you must have earned income from a foreign employer while living outside the United States for at least 330 full days in any 12-month period \u001b[0m\u001b[32m(\u001b[0m\u001b[32mor 183 days if married to a U.S. citizen or resident\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n2. **Exclusion amount**: The FEIE allows you to exclude up to $105,900 of foreign-earned income from your U.S. taxable income for tax year 2023. For tax years prior to 2018, the exclusion amount was $100,800.\\n3. **W-2 reporting**: When filing a U.S. tax return \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, you'll report your W-2 income on Line 21 of Form 1040. However, if you qualify for the FEIE, you can exclude this amount from your U.S. taxable income by completing Form 2555 and attaching it to your tax return.\\n4. **Foreign earned income**: The FEIE applies only to foreign-earned income, which includes:\\n\\t* Salary or wages\\n\\t* Other compensation \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., bonuses, commissions\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* Rent or royalty income\\n\\t* Interest on foreign debt\\n\\t* Dividend income from a foreign corporation\\n5. **Tax implications**: If you qualify for the FEIE, your W-2 income will be excluded from U.S. taxation, and you won't owe federal income tax on that amount. However, you may still owe state or local taxes on this income.\\n6. **Reporting requirements**: You must file Form 2555 with your tax return to claim the FEIE exclusion. This form requires you to provide documentation of your foreign work experience and income.\\n\\nIt's essential to note that the FEIE has some limitations and nuances, such as:\\n\\n* The exclusion amount may be reduced if you have U.S. source income \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., dividends or interest from U.S.-sourced investments\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n* You can only exclude foreign-earned income earned while living outside the United States.\\n* If you're married to a U.S. citizen or resident, your spouse's foreign-earned income is not subject to the FEIE.\\n\\nAs a tax preparer, I recommend that individuals with W-2 income from abroad consult with me to determine if they qualify for the Foreign Earned Income Exclusion and to ensure accurate reporting on their tax return.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I can tell you that a 1099-MISC form is used to report miscellaneous income that is not subject to withholding. The types of income typically reported on a 1099-MISC form include:\\n\\n1. Freelance work or independent contractor income: This includes income earned by freelancers, consultants, and independent contractors for services performed for clients.\\n2. Rent from real estate investments: Income from renting out properties, such as rental income from apartments, houses, or commercial buildings.\\n3. Royalties: Income received from the sale of intellectual property, such as music, art, literature, or inventions.\\n4. Prizes and awards: Winnings from contests, sweepstakes, or other games that are not subject to withholding.\\n5. Other miscellaneous income: This can include income from sales of goods or services that are not subject to withholding, such as bartering or commission-based income.\\n\\nThe 1099-MISC form is used by the IRS to report these types of income because it is not subject to withholding, meaning that no taxes were withheld at the source. As a result, the recipient of the income must report this income on their tax return and pay any applicable taxes, including self-employment tax.\\n\\nIt's worth noting that not all 1099-MISC forms are created equal. There are different types of 1099 forms, such as:\\n\\n* 1099-MISC: Used for miscellaneous income\\n* 1099-K: Used for payment card and third-party network transactions\\n* 1099-INT: Used for interest income\\n* 1099-DIV: Used for dividend income\\n\\nAs a tax preparer, I would work with clients to ensure they accurately report all types of income on their tax return, including those reported on a 1099-MISC form.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I can tell you that the IRS requires a 1099-MISC \u001b[0m\u001b[32m(\u001b[0m\u001b[32mMiscellaneous Income\u001b[0m\u001b[32m)\u001b[0m\u001b[32m form to be issued to independent contractors who have earned more than $600 in gross payments from a single payer during the calendar year.\\n\\nThe IRS defines an independent contractor as someone who is not considered an employee and is paid on a contract basis. This includes freelancers, consultants, independent contractors, and other self-employed individuals.\\n\\nTo qualify for a 1099-MISC form, the following conditions must be met:\\n\\n1. The payer must have paid more than $600 in gross payments to the same individual during the calendar year.\\n2. The payment is not subject to withholding \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., no taxes are withheld\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n3. The payment is made for services performed as an independent contractor.\\n\\nExamples of individuals who may receive a 1099-MISC form include:\\n\\n* Freelance writers, editors, and designers\\n* Independent contractors for construction or consulting work\\n* Self-employed artists, musicians, and performers\\n* Independent contractors for IT services\\n* Freelance photographers and videographers\\n\\nThe payer is responsible for issuing a 1099-MISC form to independent contractors by January 31st of each year, showing the amount paid to them during the previous tax year. The form must be sent to the contractor's address as it appears on file with the IRS.\\n\\nIt's worth noting that some payments may not require a 1099-MISC form, such as:\\n\\n* Payments made through a third-party payment service \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., PayPal\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* Payments made for services performed by an employee or an employee of the payer\\n* Payments made to a business entity \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., S corporation, partnership\u001b[0m\u001b[32m)\u001b[0m\u001b[32m rather than an individual\\n\\nAs a tax preparer, I would advise clients who receive 1099-MISC forms to report these payments on their tax return and pay any applicable taxes due.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I'd be happy to explain how self-employed individuals can report their business expenses on their tax return.\\n\\nSelf-employed individuals who have a business or side hustle often face unique challenges when it comes to reporting their expenses. Here's a step-by-step guide on how they can report their business expenses:\\n\\n1. **Keep accurate records**: Self-employed individuals must keep detailed and organized records of all business-related expenses, including receipts, invoices, bank statements, and credit card statements. These records should be kept for at least three years in case of an audit.\\n2. **Categorize expenses**: Business expenses can be categorized into different types, such as:\\n\\t* Operating expenses \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., rent, utilities, equipment, supplies\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* Travel expenses\\n\\t* Home office expenses \u001b[0m\u001b[32m(\u001b[0m\u001b[32mif a dedicated space is used for business purposes\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* Advertising and marketing expenses\\n\\t* Professional fees \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., lawyer, accountant, consultant\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n3. **Complete Form 1040**: Self-employed individuals report their business income and expenses on Schedule C \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, which is the form used to report net profit or loss from a business.\\n4. **Calculate business use percentage**: If you have a home office, you may be able to deduct a portion of your rent or mortgage interest as a business expense using Form 8829 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mExpenses for Business Use of Your Home\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. You'll need to calculate the business use percentage by dividing the square footage of the dedicated space used for business purposes by the total square footage of the home.\\n5. **Complete Schedule C**: On Schedule C, you'll report your business income and expenses, including:\\n\\t* Gross receipts\\n\\t* Cost of goods sold \u001b[0m\u001b[32m(\u001b[0m\u001b[32mif applicable\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* Operating expenses \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., rent, utilities, supplies\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* Travel expenses\\n\\t* Home office expenses \u001b[0m\u001b[32m(\u001b[0m\u001b[32mif applicable\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n6. **Calculate net profit or loss**: Calculate the net profit or loss from your business by subtracting total expenses from gross receipts.\\n7. **Complete Form 1040**: Report your net profit or loss on Line 21 of Form 1040.\\n8. **Claim deductions**: Claim deductions for eligible business expenses, such as:\\n\\t* Business use percentage of home office expenses \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 8829\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* Travel expenses \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 2106\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* Professional fees\\n\\t* Advertising and marketing expenses\\n9. **Keep records**: Keep all supporting documentation, including receipts, invoices, and bank statements, to support your deductions.\\n\\nSome additional tips:\\n\\n* Consult with a tax professional or accountant if you're unsure about any aspect of reporting business expenses.\\n* Consider using accounting software or apps to help track and organize your business expenses.\\n* Be aware that the IRS has specific rules and regulations regarding business expense deductions, so it's essential to follow these guidelines carefully.\\n\\nBy following these steps and keeping accurate records, self-employed individuals can ensure they're taking advantage of all eligible business expense deductions on their tax return.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"The self-employment tax rate for net earnings from self-employment is 15.3% of your net earnings from self-employment, which includes income from freelance work, consulting, or running a business.\\n\\nThis rate consists of two parts:\\n\\n1. The employee portion: 12.4% \u001b[0m\u001b[32m(\u001b[0m\u001b[32m6.2% for Social Security and 6.2% for Medicare\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n2. The employer portion: 2.9% \u001b[0m\u001b[32m(\u001b[0m\u001b[32m1.45% for Social Security and 1.45% for Medicare\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\nSince you are self-employed, you are both the employee and the employer, so you pay both parts of the tax.\\n\\nTo calculate your self-employment tax, you'll need to add the employee portion and the employer portion together:\\n\\n12.4% \u001b[0m\u001b[32m(\u001b[0m\u001b[32memployee portion\u001b[0m\u001b[32m)\u001b[0m\u001b[32m + 2.9% \u001b[0m\u001b[32m(\u001b[0m\u001b[32memployer portion\u001b[0m\u001b[32m)\u001b[0m\u001b[32m = 15.3%\\n\\nSo, for every dollar you earn from self-employment, you pay 15.3% in self-employment tax.\\n\\nKeep in mind that this rate applies to your net earnings from self-employment, which is your total income minus any business expenses and deductions.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I'd be happy to explain how self-employed individuals can calculate their self-employment tax deduction.\\n\\nThe self-employment tax is used to fund Social Security and Medicare taxes for self-employed individuals. The amount of self-employment tax you pay depends on your net earnings from self-employment, which includes income from a business or freelance work.\\n\\nHere's the step-by-step process to calculate self-employment tax:\\n\\n1. **Calculate your net earnings from self-employment**: Start by calculating your total gross income from self-employment, including income from freelancing, consulting, or running a small business.\\n2. **Deduct business expenses**: Subtract business expenses related to your self-employment from your gross income. This includes expenses such as:\\n\\t* Business use of your home \u001b[0m\u001b[32m(\u001b[0m\u001b[32mhome office deduction\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* Travel expenses\\n\\t* Equipment and supplies\\n\\t* Rent or mortgage interest\\n\\t* Utilities\\n\\t* Insurance premiums\\n\\t* Advertising and marketing expenses\\n\\t* Professional fees \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., lawyer, accountant, etc.\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n3. **Calculate your net earnings from self-employment**: Subtract the business expenses from your gross income to get your net earnings from self-employment.\\n4. **Determine your self-employment tax rate**: The self-employment tax rate is 15.3% of your net earnings from self-employment, which includes:\\n\\t* 12.4% for Social Security \u001b[0m\u001b[32m(\u001b[0m\u001b[32mold-age, survivors, and disability insurance\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* 2.9% for Medicare \u001b[0m\u001b[32m(\u001b[0m\u001b[32mhospital insurance\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n5. **Calculate your self-employment tax**: Multiply your net earnings from self-employment by the self-employment tax rate \u001b[0m\u001b[32m(\u001b[0m\u001b[32m15.3%\u001b[0m\u001b[32m)\u001b[0m\u001b[32m to calculate your self-employment tax.\\n6. **Optional: Calculate the self-employment tax deduction**: If you're eligible, you may be able to deduct half of your self-employment tax as a business expense on Schedule C \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. This can help reduce your taxable income and lower your overall tax liability.\\n\\nExample:\\n\\nLet's say John is a freelancer who earns $50,000 in gross income from his freelance work. He has business expenses of $10,000, which includes home office expenses, travel expenses, equipment, and supplies.\\n\\n1. Net earnings from self-employment: $50,000 - $10,000 = $40,000\\n2. Self-employment tax rate: 15.3% \u001b[0m\u001b[32m(\u001b[0m\u001b[32m12.4% for Social Security + 2.9% for Medicare\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n3. Self-employment tax: $40,000 x 15.3% = $6,120\\n4. Optional self-employment tax deduction: John may be able to deduct half of the self-employment tax \u001b[0m\u001b[32m(\u001b[0m\u001b[32m$6,120 / 2\u001b[0m\u001b[32m)\u001b[0m\u001b[32m as a business expense on Schedule C.\\n\\nKeep in mind that this is just an example and actual calculations may vary depending on individual circumstances. It's always best to consult with a tax professional or accountant to ensure accurate calculations and maximize your deductions.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I can tell you that yes, self-employed individuals can deduct business expenses related to their home office. This is known as the Home Office Deduction.\\n\\nThe IRS allows self-employed individuals to deduct a portion of their rent or mortgage interest and utilities as a business expense if they use a dedicated space in their home regularly and exclusively for business purposes. The amount of the deduction depends on the square footage of the home office used for business, which can be calculated using one of two methods:\\n\\n1. **Simplified Option**: This method allows self-employed individuals to deduct $5 per square foot of home office space, up to a maximum of $1,500.\\n2. **Actual Expenses Method**: This method requires calculating the actual expenses related to the home office, such as rent or mortgage interest, utilities, insurance, and maintenance costs.\\n\\nTo qualify for the Home Office Deduction, the following conditions must be met:\\n\\n* The space used for business must be a regular and exclusive use of the home.\\n* The space must be used regularly and exclusively for business purposes \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., no personal activities\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n* The space must be used in connection with the conduct of a trade or business.\\n\\nSome examples of eligible expenses that can be deducted as part of the Home Office Deduction include:\\n\\n* Rent or mortgage interest\\n* Utilities \u001b[0m\u001b[32m(\u001b[0m\u001b[32melectricity, gas, water, etc.\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* Insurance premiums\\n* Maintenance and repairs\\n* Depreciation on home office equipment\\n\\nHowever, some expenses are not eligible for deduction, such as:\\n\\n* Personal use of the space \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., a home office that is also used for personal activities like reading or watching TV\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* Improvements made to the home that benefit both business and personal use \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., installing a new kitchen sink\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\nIt's essential to keep accurate records of your home office expenses, including:\\n\\n* A log or calendar showing the dates and hours spent working from home\\n* Photos or measurements of the dedicated workspace\\n* Invoices and receipts for rent, utilities, insurance, and other expenses\\n\\nConsult with a tax professional, like myself, to ensure you're taking advantage of this valuable deduction and following the correct procedures.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I'd be happy to explain how self-employed individuals can calculate the business use percentage of their home.\\n\\nThe IRS allows self-employed individuals to deduct a portion of their rent or mortgage interest and utilities as a business expense if they use a dedicated space in their home regularly and exclusively for business. To calculate this deduction, you'll need to determine the business use percentage of your home.\\n\\nHere's a step-by-step process:\\n\\n1. **Determine the total square footage of your home**: Measure the total square footage of your home, including any additional living areas that are used for business.\\n2. **Measure the dedicated business space**: Measure the square footage of the area you use regularly and exclusively for business. This could be a home office, studio, or any other space where you conduct business activities.\\n3. **Calculate the total square footage ratio**: Divide the business space by the total home square footage to get the percentage of your home used for business.\\n\\nFor example:\\n\\n* Total home square footage: 1,500 sqft\\n* Business space \u001b[0m\u001b[32m(\u001b[0m\u001b[32mhome office\u001b[0m\u001b[32m)\u001b[0m\u001b[32m: 200 sqft\\n\\nBusiness use percentage = \u001b[0m\u001b[32m(\u001b[0m\u001b[32m200 sqft / 1,500 sqft\u001b[0m\u001b[32m)\u001b[0m\u001b[32m x 100% = 13.33%\\n\\nThis means that 13.33% of your home is used for business.\\n\\n4. **Determine the actual expenses**: Calculate the total rent or mortgage interest and utilities you pay on your home, including any additional expenses like property taxes, insurance, and maintenance.\\n5. **Calculate the business use percentage of expenses**: Multiply the total expenses by the business use percentage calculated in step 3.\\n\\nFor example:\\n\\n* Total rent/mortgage interest: $1,500 per month\\n* Business use percentage: 13.33%\\n\\nBusiness use percentage of expenses = $1,500 x 0.1333 \u001b[0m\u001b[32m(\u001b[0m\u001b[32m13.33%\u001b[0m\u001b[32m)\u001b[0m\u001b[32m = $200\\n\\nThis means that you can deduct $200 from your business income as a home office expense.\\n\\n**Important notes:**\\n\\n* You must have a dedicated space for business and use it regularly and exclusively for business.\\n* The business use percentage should be reasonable and not excessive, as the IRS may scrutinize claims with high percentages.\\n* Keep accurate records of your expenses, including receipts, invoices, and photos of your home office, to support your calculation.\\n\\nRemember to consult with a tax professional or accountant if you're unsure about how to calculate the business use percentage of your home. They can help ensure you're taking advantage of all eligible deductions and following IRS guidelines.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"The Tax Cuts and Jobs Act \u001b[0m\u001b[32m(\u001b[0m\u001b[32mTCJA\u001b[0m\u001b[32m)\u001b[0m\u001b[32m made significant changes to the home office deduction, which was previously known as the home office expense deduction or the business use percentage method. Here are some key impacts of the TCJA on the home office deduction:\\n\\n1. **Simplified Option**: The TCJA introduced a simplified option for self-employed individuals and sole proprietors to deduct a fixed amount of $5 per square foot of home office space, up to a maximum of $1,500 \u001b[0m\u001b[32m(\u001b[0m\u001b[32m$30,000 total\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. This is a flat rate that doesn't require tracking expenses or calculating the business use percentage.\\n2. **Elimination of the Business Use Percentage Method**: The TCJA eliminated the business use percentage method, which allowed self-employed individuals and sole proprietors to calculate their home office deduction based on the square footage of the space used for business. This method was phased out over a three-year period from 2018 to 2025.\\n3. **No Deduction Limitations**: The TCJA eliminated the $25,000 limitation on the home office deduction that applied to self-employed individuals and sole proprietors who were not in the active conduct of a trade or business. This means that more self-employed individuals can now deduct their home office expenses without being subject to this limit.\\n4. **No Carryover**: The TCJA eliminated the ability to carry over unused home office deductions from 2018 to 2025, which was previously allowed under the previous law.\\n\\nOverall, the simplified option provides a more straightforward and easier-to-use method for self-employed individuals and sole proprietors to deduct their home office expenses. However, it's essential to note that this new method is only available to those who are not in the active conduct of a trade or business, such as freelancers, consultants, or independent contractors.\\n\\nIt's always recommended to consult with a tax professional to determine which option is best for your specific situation and to ensure you're taking advantage of all eligible deductions.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I can tell you that yes, self-employed individuals can deduct business meals on their tax return, but there are some rules and limitations to be aware of.\\n\\nThe IRS allows self-employed individuals to deduct the cost of business meals as a miscellaneous itemized deduction on Schedule C \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, which is used for sole proprietorships and single-member limited liability companies \u001b[0m\u001b[32m(\u001b[0m\u001b[32mLLCs\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n\\nTo qualify for this deduction, the meal must meet certain requirements:\\n\\n1. The meal must be for business or business purposes.\\n2. The meal must be with a client, customer, or prospective client.\\n3. The meal cannot be primarily for entertainment or recreation.\\n\\nHere are some examples of eligible meals:\\n\\n* Business lunches with clients or customers\\n* Breakfast meetings with potential clients\\n* Traveling to and from a meeting or conference\\n* Meals at conferences or trade shows\\n\\nHowever, the following types of meals are not eligible for deduction:\\n\\n* Social gatherings, such as birthday parties or holiday celebrations\\n* Meals that are primarily for entertainment or recreation\\n* Meals that are not related to business activities\\n\\nTo deduct business meals, you'll need to keep accurate records, including:\\n\\n1. Receipts and invoices from the restaurant or catering service\\n2. A log of the date, time, location, and purpose of each meal\\n3. The names and titles of the individuals present \u001b[0m\u001b[32m(\u001b[0m\u001b[32mif applicable\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\nThe IRS allows a standard deduction of $5 per meal for meals with clients or customers, but this can be adjusted based on the cost of the meal.\\n\\nIt's also worth noting that the Tax Cuts and Jobs Act \u001b[0m\u001b[32m(\u001b[0m\u001b[32mTCJA\u001b[0m\u001b[32m)\u001b[0m\u001b[32m suspended the 50% limit on business meal deductions from 2018 to 2025. However, after 2025, the 50% limit will return.\\n\\nAs a tax preparer, I always recommend keeping accurate records and consulting with a tax professional to ensure you're taking advantage of all eligible deductions and following the correct procedures for claiming business meals on your tax return.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I'd be happy to explain how self-employed individuals can report 1099 income from a partnership.\\n\\nWhen you're a partner in a partnership, you receive a Form 1099-K from the partnership at the end of each year. This form shows the total amount of money you received from the partnership during the tax year. However, as a self-employed individual, you need to report this income on your personal tax return.\\n\\nHere's how to report 1099 income from a partnership:\\n\\n1. **Report the income on Schedule C \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m**: You'll report the 1099-K income on Schedule C \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, which is the form used for self-employment income and expenses.\\n2. **Complete Form 1065**: As a partner, you're also required to file a partnership return with the IRS using Form 1065. This form reports the partnership's income, deductions, and credits. You'll need to attach this form to your personal tax return \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n3. **Report business use of home**: If you used a dedicated space in your home for business purposes, you may be able to deduct a portion of your rent or mortgage interest as a business expense on Schedule C.\\n4. **Business expenses**: You can also report business-related expenses on Schedule C, such as travel expenses, equipment purchases, and supplies.\\n5. **Self-employment tax**: As a self-employed individual, you're responsible for paying self-employment tax \u001b[0m\u001b[32m(\u001b[0m\u001b[32mSE tax\u001b[0m\u001b[32m)\u001b[0m\u001b[32m on your net earnings from self-employment. This is reported on Schedule SE \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n6. **Estimated tax payments**: If you expect to owe more than $1,000 in taxes for the year, you may need to make estimated tax payments throughout the year using Form 1040-ES.\\n\\nSome important notes:\\n\\n* You'll need to keep accurate records of your partnership income and expenses, as well as any business-related documents, such as invoices, receipts, and bank statements.\\n* If you're a partner in a limited liability company \u001b[0m\u001b[32m(\u001b[0m\u001b[32mLLC\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, you may be able to report the income on Schedule C or Form 1040, depending on how the LLC is structured.\\n* It's always a good idea to consult with a tax professional or accountant to ensure you're meeting all the necessary reporting requirements and taking advantage of available deductions.\\n\\nI hope this helps! Let me know if you have any other questions.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"The penalty for not reporting 1099 income on a tax return can vary depending on several factors, including the amount of unreported income, the taxpayer's intent, and whether they have made an honest effort to comply with their tax obligations.\\n\\n Generally, the IRS imposes penalties for failing to report 1099 income on Form 1040. The penalty is calculated as follows:\\n\\n1. The first $500 of unreported 1099 income is not subject to penalty.\\n2. For amounts between $500 and $5,000, the penalty is 20% of the amount of unreported income.\\n3. For amounts over $5,000, the penalty is 40% of the amount of unreported income.\\n\\nIn addition to the penalty, you may also be subject to interest on the unreported income from the date it was due.\\n\\nIt's worth noting that there are some exceptions and mitigating factors that can affect the penalty, such as:\\n\\n* If you have an honest effort to comply with your tax obligations, but made a reasonable mistake or error.\\n* If you have filed Form 2210, which is used to request abatement of penalties for failure to report income.\\n* If you are a first-time filer and meet certain requirements.\\n\\nIt's always best to consult with a tax professional or the IRS directly to determine the specific penalty and any potential relief options.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'As a tax preparer, I\\'d be happy to help clarify this for you.\\n\\nSelf-employed individuals can indeed deduct self-employment tax on their tax return, but there are some important nuances to understand.\\n\\nThe Self-Employment Tax \u001b[0m\u001b[32m(\u001b[0m\u001b[32mSE\u001b[0m\u001b[32m)\u001b[0m\u001b[32m is a type of payroll tax that covers Social Security and Medicare taxes. As a self-employed individual, you\\'re responsible for paying both the employer and employee portions of these taxes, which is why it\\'s called \"self-employment tax.\"\\n\\nTo deduct self-employment tax on your tax return, you\\'ll need to calculate the net earnings from self-employment and then subtract any qualified retirement plan contributions. Here are the steps:\\n\\n1. Calculate your net earnings from self-employment: This includes income from your business or freelance work, minus any business expenses.\\n2. Determine your self-employment tax liability: You can use Form 1040 to calculate this amount using Schedule SE \u001b[0m\u001b[32m(\u001b[0m\u001b[32mSelf-Employment Tax\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n3. Subtract qualified retirement plan contributions: If you made contributions to a SEP-IRA, solo 401\u001b[0m\u001b[32m(\u001b[0m\u001b[32mk\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, or other qualified plans, you can subtract these contributions from your net earnings from self-employment.\\n4. Calculate the self-employment tax deduction: This is the amount of self-employment tax you paid during the year.\\n\\nThe standard rate for self-employment tax is 15.3% of your net earnings from self-employment, which includes:\\n\\n* 12.4% for Social Security \u001b[0m\u001b[32m(\u001b[0m\u001b[32mold-age, survivors, and disability insurance\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* 2.9% for Medicare \u001b[0m\u001b[32m(\u001b[0m\u001b[32mhospital insurance\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\nHowever, you may be able to deduct half of this amount as a business expense on Schedule C \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, which can help reduce your taxable income.\\n\\nIt\\'s essential to note that the self-employment tax deduction is subject to certain limits and phase-outs. For example:\\n\\n* The net earnings from self-employment limit: If your net earnings from self-employment exceed $400, you\\'re required to make estimated tax payments throughout the year.\\n* Phase-out of self-employment tax deduction: If your adjusted gross income exceeds a certain threshold \u001b[0m\u001b[32m(\u001b[0m\u001b[32mcurrently $160,200 for single filers and $320,400 for joint filers\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, the self-employment tax deduction may be phased out.\\n\\nTo ensure accurate calculations and compliance with IRS regulations, it\\'s always best to consult with a tax professional or use tax preparation software that can guide you through the process.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I've seen my fair share of missing or incorrect 1099 forms from self-employed individuals. Here's how they typically handle these situations:\\n\\n**Missing 1099 Form:**\\n\\nIf a self-employed individual receives a missing 1099 form, they should follow these steps:\\n\\n1. **Contact the payer**: Reach out to the payer \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., client, contractor, or freelancer\u001b[0m\u001b[32m)\u001b[0m\u001b[32m and ask for a replacement copy of the 1099 form.\\n2. **Request an amended 1099**: If the payer is unable to provide a replacement copy, request that they file an amended 1099 with the IRS by the original filing deadline \u001b[0m\u001b[32m(\u001b[0m\u001b[32musually April 15th\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n3. **File Form 4852**: The self-employed individual may need to complete Form 4852, Substitute for Form W-2, Wage and Tax Statement, if they don't receive a 1099 form from their payer.\\n4. **Report income on Schedule C**: On their tax return \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, the self-employed individual will report the missing income on Schedule C \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, which is the business income and expenses schedule.\\n\\n**Incorrect 1099 Form:**\\n\\nIf a self-employed individual receives an incorrect 1099 form, they should:\\n\\n1. **Review the form carefully**: Check for any errors or discrepancies in the information reported.\\n2. **Contact the payer**: Reach out to the payer and request that they correct the error\u001b[0m\u001b[32m(\u001b[0m\u001b[32ms\u001b[0m\u001b[32m)\u001b[0m\u001b[32m on the 1099 form.\\n3. **Request a corrected 1099**: If the payer is unable to correct the error, ask them to file an amended 1099 with the IRS by the original filing deadline \u001b[0m\u001b[32m(\u001b[0m\u001b[32musually April 15th\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n4. **Report income correctly on Schedule C**: On their tax return \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, the self-employed individual will report the corrected income on Schedule C.\\n\\n**Additional Tips:**\\n\\n* Self-employed individuals should keep a record of all correspondence with their payer, including dates and details of conversations or emails.\\n* If the error is significant \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., incorrect amount or type of income\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, it may be beneficial to seek professional help from a tax preparer or accountant to ensure accurate reporting on their tax return.\\n* In some cases, self-employed individuals may need to file Form 1040X \u001b[0m\u001b[32m(\u001b[0m\u001b[32mAmended U.S. Individual Income Tax Return\u001b[0m\u001b[32m)\u001b[0m\u001b[32m if they discover errors or discrepancies after filing their original tax return.\\n\\nBy following these steps, self-employed individuals can minimize the impact of a missing or incorrect 1099 form and ensure accurate reporting on their tax return.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I can tell you that yes, self-employed individuals can amend their tax return if they receive a corrected 1099 form.\\n\\nIf a self-employed individual receives a corrected 1099 form from an employer or client, it's essential to file an amended tax return \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040X\u001b[0m\u001b[32m)\u001b[0m\u001b[32m to reflect the corrected income. Here are some scenarios where amending is necessary:\\n\\n1. **Corrected income**: If the corrected 1099 form shows that you received more or less income than initially reported on your original tax return, you'll need to amend your return to reflect the correct amount.\\n2. **Incorrect income reporting**: If the corrected 1099 form indicates an error in the amount of income reported, such as a miscalculation or incorrect payment, you should file an amended return to correct this discrepancy.\\n3. **Missing income**: If the corrected 1099 form reveals that you missed reporting any income on your original tax return, you'll need to amend your return to include this additional income.\\n\\nTo amend your tax return, follow these steps:\\n\\n1. Gather all relevant documents, including the corrected 1099 form and any other supporting documentation.\\n2. Complete Form 1040X, which is the amended U.S. Individual Income Tax Return.\\n3. Attach a copy of the corrected 1099 form to the amended return.\\n4. File the amended return with the IRS by the original filing deadline \u001b[0m\u001b[32m(\u001b[0m\u001b[32musually April 15th for individual tax returns\u001b[0m\u001b[32m)\u001b[0m\u001b[32m or within three years from the original filing date, whichever is later.\\n\\nKeep in mind that you'll need to provide documentation to support your amended return, such as:\\n\\n* The corrected 1099 form\\n* Any other relevant financial records, like bank statements or cancelled checks\\n* A written explanation of the error and how it was corrected\\n\\nIt's essential to note that amending a tax return can be complex, so if you're unsure about the process or need help with the amended return, consider consulting a tax professional, such as myself!\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I can tell you that the deadline for receiving a 1099 form varies depending on the type of income and the payer.\\n\\nFor most types of income, such as freelance work, independent contracting, or self-employment income, the deadline for receiving a 1099-MISC \u001b[0m\u001b[32m(\u001b[0m\u001b[32mMiscellaneous Income\u001b[0m\u001b[32m)\u001b[0m\u001b[32m form is January 31st of each year. This means that by January 31st, you should receive a copy of your 1099-MISC from any payer who paid you $600 or more in a calendar year.\\n\\nHowever, there are some exceptions to this deadline:\\n\\n* For payments made through a third-party payment service, such as PayPal or Venmo, the deadline is February 1st.\\n* For payments made by a corporation, the deadline is January 31st for corporations that file Form 1099-K \u001b[0m\u001b[32m(\u001b[0m\u001b[32mPayment Card and Third-Party Network Transactions\u001b[0m\u001b[32m)\u001b[0m\u001b[32m with the IRS.\\n* For payments made to non-resident aliens, the deadline is March 15th.\\n\\nIt's also worth noting that some states may have different deadlines for receiving 1099 forms. As a tax preparer, I would recommend checking with your state's tax authority to confirm their specific deadline.\\n\\nAs a general rule of thumb, it's always best to receive your 1099 form by January 31st to ensure you can accurately report your income on your tax return and avoid any potential penalties or interest.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I'd be happy to explain how self-employed individuals can report 1099 income on their tax return.\\n\\nSelf-employment income is reported on Schedule C \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, which is the form used for sole proprietorships and single-member limited liability companies \u001b[0m\u001b[32m(\u001b[0m\u001b[32mLLCs\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. Here's a step-by-step guide:\\n\\n1. **Gather all 1099 forms**: Collect all 1099-MISC forms from clients, customers, or vendors who paid you $600 or more in a calendar year. These forms will show the amount of money you earned from each client.\\n2. **Calculate business income**: Add up the total amount of 1099 income received throughout the year. This includes income from freelance work, consulting, selling products or services, and any other self-employment activities.\\n3. **Complete Schedule C \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m**: On Schedule C, report your business income on Line 1. You'll also need to calculate your business expenses on this form, which will be discussed later.\\n4. **Calculate net profit or loss**: Subtract your business expenses from your business income on Line 2 of Schedule C. This will give you your net profit or loss for the year.\\n5. **Complete Form 1040**: Report your net profit or loss from Schedule C on Line 31 of Form 1040. If you have a net profit, this amount will be reported as ordinary income on your tax return.\\n\\n**Business Expenses:**\\n\\nAs a self-employed individual, you can deduct business expenses on Schedule C to reduce your taxable income. Common business expenses include:\\n\\n* Home office expenses \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., rent, utilities, equipment\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* Travel expenses\\n* Business use of your car or other vehicles\\n* Meals and entertainment expenses \u001b[0m\u001b[32m(\u001b[0m\u001b[32msubject to certain limits\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* Business-related travel expenses\\n* Professional fees \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., lawyer, accountant, insurance\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\n**Self-Employment Tax:**\\n\\nAs a self-employed individual, you're responsible for paying self-employment tax on your net earnings from self-employment. This tax is used to fund Social Security and Medicare. The self-employment tax rate is 15.3% of your net earnings from self-employment, which includes:\\n\\n* 12.4% for Social Security \u001b[0m\u001b[32m(\u001b[0m\u001b[32mold-age, survivors, and disability insurance\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* 2.9% for Medicare \u001b[0m\u001b[32m(\u001b[0m\u001b[32mhospital insurance\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\nYou'll report this tax on Schedule SE \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, which is attached to Form 1040.\\n\\n**Estimated Tax Payments:**\\n\\nAs a self-employed individual, you're required to make estimated tax payments throughout the year if you expect to owe $1,000 or more in taxes. You can use Form 1040-ES to make these payments.\\n\\nThat's a general overview of how self-employed individuals report 1099 income on their tax return. If you have any specific questions or concerns, it's always best to consult with a tax professional like myself for personalized guidance!\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I'd be happy to help clarify this for you.\\n\\nYes, self-employed individuals can deduct business expenses on their 1099 income. In fact, one of the benefits of being self-employed is that you can deduct business expenses related to your trade or business as an expense on your tax return.\\n\\nThe IRS allows self-employed individuals to deduct business expenses on Schedule C \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, which is used to report business income and expenses. This includes:\\n\\n1. Business use of your home: If you use a dedicated space in your home for business, you can deduct the business use percentage of your rent or mortgage interest, utilities, and other expenses.\\n2. Business travel expenses: You can deduct expenses related to business travel, such as transportation, meals, lodging, and entertainment.\\n3. Equipment and supplies: You can deduct the cost of equipment, software, and supplies used for your business.\\n4. Advertising and marketing expenses: You can deduct expenses related to promoting your business, such as website development, advertising, and promotional materials.\\n5. Business use of your car: If you use your car for business purposes, you can deduct the business use percentage of your car expenses, including gas, maintenance, and insurance.\\n6. Professional fees: You can deduct fees paid to professionals, such as lawyers, accountants, and consultants, who provide services related to your business.\\n7. Business education and training: You can deduct expenses related to courses or workshops that improve your skills or knowledge in your trade or business.\\n\\nTo qualify for these deductions, you must have records to support the expense, including receipts, invoices, and bank statements. It's also important to keep accurate records of your business income and expenses throughout the year, as this will help you complete your tax return accurately and avoid any potential audits.\\n\\nSome important notes:\\n\\n* You can only deduct expenses that are directly related to your business.\\n* You cannot deduct personal expenses, such as charitable donations or medical expenses, unless they are also business-related.\\n* The IRS has specific rules for deducting home office expenses, including the 5% rule, which allows you to deduct a portion of your rent or mortgage interest based on the square footage used for business.\\n\\nIt's always a good idea to consult with a tax professional, like myself, to ensure you're taking advantage of all the deductions available to you and following the IRS guidelines.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I'd be happy to explain how self-employed individuals calculate their self-employment tax.\\n\\nSelf-employment tax is used to fund Social Security and Medicare, just like payroll taxes for employees. However, self-employed individuals are responsible for paying both the employee and employer portions of these taxes, which can add up quickly.\\n\\nHere's a step-by-step guide on how self-employed individuals calculate their self-employment tax:\\n\\n1. **Calculate your net earnings from self-employment**: Start by calculating your total income from all sources related to your business or freelance work. This includes:\\n\\t* Business income \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., cash, checks, credit card payments\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* Freelance income\\n\\t* Rent or royalty income\\n\\t* Any other income related to your business\\n2. **Deduct business expenses**: Subtract business expenses from your total income to determine your net earnings from self-employment. This will help reduce your taxable income.\\n3. **Calculate the self-employment tax rate**: The self-employment tax rate is 15.3% of your net earnings from self-employment, which includes:\\n\\t* 12.4% for Social Security \u001b[0m\u001b[32m(\u001b[0m\u001b[32mold-age, survivors, and disability insurance\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* 2.9% for Medicare \u001b[0m\u001b[32m(\u001b[0m\u001b[32mhospital insurance\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n4. **Calculate the self-employment tax**: Multiply your net earnings from self-employment by the self-employment tax rate \u001b[0m\u001b[32m(\u001b[0m\u001b[32m15.3%\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. This will give you the total amount of self-employment tax due.\\n5. **Add half of your Social Security tax to your income**: Since self-employed individuals pay both the employee and employer portions of payroll taxes, you'll need to add half of your Social Security tax to your income. This is calculated as:\\n\\t* 6.2% of your net earnings from self-employment \u001b[0m\u001b[32m(\u001b[0m\u001b[32mhalf of the 12.4% rate\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n6. **Calculate your total self-employment tax**: Add the self-employment tax and the additional Social Security tax to get your total self-employment tax liability.\\n\\nExample:\\n\\nLet's say you have a net income from self-employment of $50,000 and business expenses of $20,000, leaving you with $30,000 in taxable income. Your self-employment tax rate is 15.3%.\\n\\n1. Calculate the self-employment tax: $30,000 x 15.3% = $4,590\\n2. Add half of your Social Security tax: $30,000 x 6.2% = $1,860\\n3. Calculate your total self-employment tax: $4,590 + $1,860 = $6,450\\n\\nIn this example, the self-employed individual would need to pay a total of $6,450 in self-employment tax.\\n\\nKeep in mind that you can deduct half of your self-employment tax as a business expense on Schedule C \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, which can help reduce your taxable income. It's always a good idea to consult with a tax professional or accountant to ensure accurate calculations and to take advantage of any available deductions.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'As a tax preparer, I can tell you that self-employment tax applies to income from various sources, including:\\n\\n1. **Business income**: Income earned from running your own business, such as freelancing, consulting, or starting a side hustle.\\n2. **Self-employment income**: Income earned from working for yourself, such as:\\n\\t* Independent contractor work\\n\\t* Freelance writing, designing, or other creative services\\n\\t* Renting out a room on Airbnb\\n\\t* Selling products online through an e-commerce platform\\n3. **Unemployment benefits**: Some states tax unemployment benefits as self-employment income.\\n4. **Alimony paid to ex-spouses**: Alimony payments made by one spouse to the other are considered self-employment income and subject to self-employment tax.\\n5. **Royalties**: Income from intellectual property, such as book royalties or music royalties, is also subject to self-employment tax.\\n\\nSelf-employment tax applies because you\\'re considered self-employed and must report this income on your tax return. As a self-employed individual, you\\'re responsible for paying both the employee and employer portions of payroll taxes, which includes:\\n\\n* 12.4% for Social Security \u001b[0m\u001b[32m(\u001b[0m\u001b[32mold-age, survivors, and disability insurance\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* 2.9% for Medicare \u001b[0m\u001b[32m(\u001b[0m\u001b[32mhospital insurance\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\nThis total is often referred to as your \"self-employment tax rate.\" You\\'ll need to pay this amount on a quarterly basis using Form 1040-ES.\\n\\nKeep in mind that some states may have different rules or exemptions from self-employment tax, so it\\'s always best to consult with a tax professional or check with your state\\'s tax authority for specific guidance.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I'd be happy to explain how self-employed individuals can report their business income and expenses.\\n\\nSelf-employed individuals who have a business or freelance work must report their income and expenses on their personal tax return. Here's a step-by-step guide:\\n\\n**Reporting Business Income:**\\n\\n1. **Business Income:** Self-employed individuals must report all business income, including:\\n\\t* Cash payments from clients\\n\\t* Accounts receivable \u001b[0m\u001b[32m(\u001b[0m\u001b[32mamounts owed to them by customers\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* Interest income from business-related investments\\n\\t* Royalties or other passive income\\n2. **Self-Employment Tax:** If you're self-employed, you'll need to pay self-employment tax on your net earnings from self-employment. This includes:\\n\\t* Net earnings from self-employment \u001b[0m\u001b[32m(\u001b[0m\u001b[32mbusiness income minus business expenses\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* Half of your net earnings from self-employment \u001b[0m\u001b[32m(\u001b[0m\u001b[32mfor Social Security and Medicare taxes\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\n**Reporting Business Expenses:**\\n\\n1. **Business Expense Records:** Keep accurate records of all business-related expenses, including:\\n\\t* Receipts\\n\\t* Invoices\\n\\t* Bank statements\\n\\t* Credit card statements\\n2. **Business Expense Categories:** Categorize your expenses into the following categories:\\n\\t* Operating expenses \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., rent, utilities, supplies\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* Business use of your home \u001b[0m\u001b[32m(\u001b[0m\u001b[32mif you work from home\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* Travel expenses\\n\\t* Advertising and marketing expenses\\n\\t* Professional fees \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., lawyer, accountant\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n3. **Business Expense Deductions:** Claim deductions for business expenses that are ordinary and necessary for the operation of your business.\\n\\n**Common Business Expense Deductions:**\\n\\n1. Home office deduction \u001b[0m\u001b[32m(\u001b[0m\u001b[32mif you work from home\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n2. Business use of your car\\n3. Travel expenses \u001b[0m\u001b[32m(\u001b[0m\u001b[32mmileage, meals, lodging\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n4. Professional fees \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., lawyer, accountant\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n5. Advertising and marketing expenses\\n\\n**Reporting Business Expenses on the Tax Return:**\\n\\n1. **Schedule C \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m:** Complete Schedule C to report business income and expenses.\\n2. **Business Use of Your Home:** If you work from home, complete Form 8829 to calculate your home office deduction.\\n3. **Business Expense Deductions:** Report business expense deductions on Schedule A \u001b[0m\u001b[32m(\u001b[0m\u001b[32mItemized Deductions\u001b[0m\u001b[32m)\u001b[0m\u001b[32m or on a separate form \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., Form 2106 for car expenses\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n\\n**Important Notes:**\\n\\n1. Keep accurate records of all business income and expenses throughout the year, as these will be used to complete your tax return.\\n2. Consult with a tax professional if you're unsure about any aspect of reporting business income and expenses.\\n3. Self-employed individuals may need to file additional forms, such as Form 1040-ES \u001b[0m\u001b[32m(\u001b[0m\u001b[32mEstimated Tax for Individuals\u001b[0m\u001b[32m)\u001b[0m\u001b[32m or Schedule SE \u001b[0m\u001b[32m(\u001b[0m\u001b[32mSelf-Employment Tax\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n\\nRemember, accurate and timely reporting of business income and expenses is crucial to avoid penalties and interest on underreported income or unclaimed deductions.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"The self-employment tax rate for net earnings from self-employment is 15.3% of your net earnings from self-employment, which includes income from freelance work, consulting, or running a business.\\n\\nThis rate consists of two parts:\\n\\n1. The employee portion: 12.4% \u001b[0m\u001b[32m(\u001b[0m\u001b[32m6.2% for Social Security and 6.2% for Medicare\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n2. The employer portion: 2.9% \u001b[0m\u001b[32m(\u001b[0m\u001b[32m1.45% for Social Security and 1.45% for Medicare\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\nSince you are self-employed, you are both the employee and the employer, so you pay both parts of the tax.\\n\\nTo calculate your self-employment tax, you'll need to add the employee portion and the employer portion together:\\n\\n12.4% \u001b[0m\u001b[32m(\u001b[0m\u001b[32memployee portion\u001b[0m\u001b[32m)\u001b[0m\u001b[32m + 2.9% \u001b[0m\u001b[32m(\u001b[0m\u001b[32memployer portion\u001b[0m\u001b[32m)\u001b[0m\u001b[32m = 15.3%\\n\\nSo, for every dollar you earn from self-employment, you pay 15.3% in self-employment tax.\\n\\nKeep in mind that this rate applies to your net earnings from self-employment, which is your total income minus any business expenses and deductions.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I can tell you that yes, self-employed individuals can deduct business use of their home as a business expense, but there are some requirements and limitations to be aware of.\\n\\nTo qualify for the home office deduction, the space used for business must meet certain criteria:\\n\\n1. **Business use percentage**: The space must be used regularly and exclusively for business purposes. This means that you can't simply convert a spare room into a home office just to claim a deduction.\\n2. **Business use of at least 5%**: The space must be used by the self-employed individual for business purposes for at least 5% of the total square footage of the home.\\n3. **Home office is used as a regular and necessary business expense**: The home office must be used regularly and be necessary for the conduct of your trade or business.\\n\\nTo calculate the deduction, you'll need to determine the business use percentage of your home. You can do this by:\\n\\n1. Measuring the square footage of the space used for business.\\n2. Calculating the total square footage of your home.\\n3. Dividing the business use square footage by the total square footage.\\n\\nFor example, if your home office is 100 square feet and your total home size is 1,500 square feet, you can calculate the business use percentage as follows:\\n\\nBusiness use percentage = \u001b[0m\u001b[32m(\u001b[0m\u001b[32m100 sq ft / 1,500 sq ft\u001b[0m\u001b[32m)\u001b[0m\u001b[32m x 100% = 6.67%\\n\\nOnce you have the business use percentage, you can deduct a portion of your rent or mortgage interest and utilities as a business expense.\\n\\n**Types of expenses that can be deducted:**\\n\\n* Rent or mortgage interest\\n* Property taxes \u001b[0m\u001b[32m(\u001b[0m\u001b[32mif not included in the mortgage\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* Utilities \u001b[0m\u001b[32m(\u001b[0m\u001b[32melectricity, gas, water, internet, etc.\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* Home maintenance and repairs\\n\\n**Record-keeping is key:**\\n\\nTo support your home office deduction, keep accurate records of:\\n\\n1. Business use percentage calculations\\n2. Square footage measurements\\n3. Rent or mortgage statements\\n4. Utility bills\\n5. Maintenance and repair receipts\\n\\nIt's essential to maintain these records for at least 3 years in case of an audit.\\n\\n**Important notes:**\\n\\n* The home office deduction is subject to the $25,000 limit per year \u001b[0m\u001b[32m(\u001b[0m\u001b[32mpre-2018\u001b[0m\u001b[32m)\u001b[0m\u001b[32m or $10,000 limit per year \u001b[0m\u001b[32m(\u001b[0m\u001b[32mpost-2017\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n* If you're married and file jointly, you can deduct half of the business use percentage.\\n* You may need to complete Form 8829 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mExpenses for Business Use of Your Home\u001b[0m\u001b[32m)\u001b[0m\u001b[32m to claim the deduction.\\n\\nAs a tax preparer, I recommend consulting with me or a qualified tax professional to ensure you meet all the requirements and follow the correct procedures for claiming the home office deduction.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I'd be happy to explain how self-employed individuals calculate their net earnings from self-employment for self-employment tax purposes.\\n\\nSelf-employment income is subject to both the employee and employer portions of payroll taxes, which includes Social Security and Medicare taxes. As a self-employed individual, you are responsible for paying both the employee and employer portions of these taxes, which can be a bit more complicated than just taking the standard deduction.\\n\\nTo calculate net earnings from self-employment, follow these steps:\\n\\n1. **Calculate your total gross income**: Start by calculating your total gross income from all sources, including:\\n\\t* Business income \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., freelance work, consulting, or running a business\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* Rent or royalty income\\n\\t* Interest, dividends, and capital gains\\n2. **Deduct business expenses**: Subtract business expenses related to your self-employment income, such as:\\n\\t* Business use of your home \u001b[0m\u001b[32m(\u001b[0m\u001b[32mhome office deduction\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* Travel expenses\\n\\t* Equipment, supplies, and materials\\n\\t* Rent or mortgage interest\\n\\t* Utilities\\n\\t* Insurance premiums\\n\\t* Advertising and marketing expenses\\n\\t* Professional fees \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., lawyer, accountant, or consultant fees\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n3. **Calculate net earnings from self-employment**: Subtract your business expenses from your total gross income to get your net earnings from self-employment.\\n4. **Calculate the self-employment tax**: Calculate the self-employment tax by using Schedule SE \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m and the following formula:\\n\\nSelf-Employment Tax = Net Earnings from Self-Employment x 15.3% \u001b[0m\u001b[32m(\u001b[0m\u001b[32m12.4% for Social Security + 2.9% for Medicare\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\nThis rate is applied to your net earnings from self-employment, but you can deduct half of this amount as a credit on Schedule SE.\\n\\n5. **Calculate the self-employment tax deduction**: You can deduct half of your self-employment tax as an above-the-line deduction on Form 1040, which reduces your taxable income.\\n6. **Report net earnings from self-employment on Schedule C \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m**: Report your net earnings from self-employment on Schedule C, which is the business income and expense schedule.\\n\\nExample:\\n\\nLet's say John is a freelancer who earns $50,000 in gross income from his freelance work. He has business expenses of $15,000, including home office expenses, travel expenses, and equipment purchases. His net earnings from self-employment would be:\\n\\nNet Earnings from Self-Employment = Gross Income - Business Expenses\\\u001b[0m\u001b[32mn\u001b[0m\u001b[32m= $50,000 - $15,000\\\u001b[0m\u001b[32mn\u001b[0m\u001b[32m= $35,000\\n\\nTo calculate the self-employment tax:\\n\\nSelf-Employment Tax = Net Earnings from Self-Employment x 15.3%\\\u001b[0m\u001b[32mn\u001b[0m\u001b[32m= $35,000 x 0.153\\\u001b[0m\u001b[32mn\u001b[0m\u001b[32m= $5,405\\n\\nJohn would report his net earnings from self-employment on Schedule C and pay self-employment tax of $5,405. He can deduct half of this amount as a credit on Schedule SE.\\n\\nKeep in mind that this is just an example, and your specific situation may be more complex. It's always best to consult with a tax professional or accountant to ensure you're accurately calculating your net earnings from self-employment and taking advantage of all the deductions available to you.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'As a tax preparer, I can tell you that yes, self-employed individuals can deduct their health insurance premiums as a business expense on their tax return.\\n\\nThe IRS allows self-employed individuals to deduct the cost of health insurance premiums for themselves and their family members as a business expense if they are required to pay these premiums because of their self-employment income. This is known as the \"self-employment health plan deduction.\"\\n\\nTo qualify for this deduction, you must meet certain requirements:\\n\\n1. You must be self-employed and have net earnings from self-employment of $100 or more.\\n2. You must purchase a qualified health insurance policy that covers you and your family members.\\n3. The policy must be purchased through the Health Insurance Marketplace \u001b[0m\u001b[32m(\u001b[0m\u001b[32malso known as an \"individual shared responsibility payment\"\u001b[0m\u001b[32m)\u001b[0m\u001b[32m or through a group plan offered by an employer.\\n\\nThe deduction is calculated based on the amount of premiums paid for yourself, your spouse, and any dependents who are covered under the policy. You can deduct the full premium amount, but you may need to adjust it if you have other sources of income that reduce your self-employment net earnings from self-employment.\\n\\nHere\\'s an example:\\n\\nLet\\'s say John is a sole proprietor with $50,000 in net earnings from self-employment and he pays $1,500 per month for health insurance premiums. He can deduct the full $1,500 as a business expense on his tax return.\\n\\nHowever, if John has other sources of income that reduce his net earnings from self-employment to $40,000, he can only deduct the amount of the premium that reduces his net earnings by $10,000 \u001b[0m\u001b[32m(\u001b[0m\u001b[32m$50,000 - $40,000 = $10,000\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. In this case, John would deduct $1,500 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mthe full premium\u001b[0m\u001b[32m)\u001b[0m\u001b[32m minus $10,000 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mthe reduced net earnings\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, which is $900.\\n\\nIt\\'s always a good idea to keep accurate records of your health insurance premiums and other business expenses to ensure you can accurately calculate the deduction on your tax return.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'As a tax preparer, I\\'d be happy to explain the differences between a sole proprietorship and a single-member Limited Liability Company \u001b[0m\u001b[32m(\u001b[0m\u001b[32mLLC\u001b[0m\u001b[32m)\u001b[0m\u001b[32m for tax purposes.\\n\\n**Sole Proprietorship:**\\n\\nA sole proprietorship is a business owned and operated by one individual. The owner reports their business income on their personal tax return \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. As a sole proprietor, the business income is reported as \"net earnings from self-employment\" on Schedule C \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, which is attached to the personal tax return.\\n\\nThe key characteristics of a sole proprietorship for tax purposes are:\\n\\n* The owner reports all business income and expenses on their personal tax return.\\n* Business losses can be used to offset other income, but not carried over to future years.\\n* Self-employment taxes \u001b[0m\u001b[32m(\u001b[0m\u001b[32mSocial Security and Medicare taxes\u001b[0m\u001b[32m)\u001b[0m\u001b[32m are calculated based on net earnings from self-employment.\\n\\n**Single-Member LLC:**\\n\\nA single-member Limited Liability Company \u001b[0m\u001b[32m(\u001b[0m\u001b[32mLLC\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, also known as a \"disregarded entity,\" is a business owned by one individual. For tax purposes, the single-member LLC is treated as a sole proprietorship. The owner reports all business income and expenses on their personal tax return \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n\\nHowever, there are some key differences:\\n\\n* A single-member LLC can elect to be taxed as a pass-through entity, meaning that the business income is passed through to the individual\\'s personal tax return, just like a sole proprietorship.\\n* Business losses can be carried over to future years and used to offset other income.\\n* Self-employment taxes are calculated based on net earnings from self-employment.\\n\\n**Key differences:**\\n\\nThe main difference between a single-member LLC and a sole proprietorship is the level of liability protection. As a sole proprietor, the owner\\'s personal assets are at risk in case of business debts or lawsuits. In contrast, a single-member LLC provides some level of liability protection, as the business is separate from the individual.\\n\\nHowever, for tax purposes, a single-member LLC and a sole proprietorship are treated similarly. The business income is reported on the same tax return, and self-employment taxes are calculated in the same way.\\n\\n**When to choose an LLC:**\\n\\nWhile a single-member LLC may not provide significant tax benefits over a sole proprietorship, there are situations where it might be beneficial:\\n\\n* Liability protection: If you want to protect your personal assets from business debts or lawsuits.\\n* Flexibility: An LLC can elect to be taxed as a pass-through entity, which allows for more control over taxes and flexibility in the future.\\n\\nIn summary, while both sole proprietorships and single-member LLCs are treated similarly for tax purposes, an LLC provides some level of liability protection that may be attractive to business owners. However, the tax benefits are relatively minor, and the decision ultimately depends on your individual circumstances and goals.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I'd be happy to explain how self-employed individuals can report self-employment income from a partnership.\\n\\nWhen it comes to reporting self-employment income from a partnership, there are some specific rules and requirements that need to be followed. Here's a step-by-step guide:\\n\\n1. **Form 1065**: The partnership must file Form 1065, U.S. Return of Partnership Income \u001b[0m\u001b[32m(\u001b[0m\u001b[32mInformation\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, with the IRS by March 15th of each year. This form reports the partnership's income, deductions, and credits.\\n2. **K-1 Forms**: Each partner receives a Schedule K-1 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1065\u001b[0m\u001b[32m)\u001b[0m\u001b[32m from the partnership, which shows their share of the partnership's income, deductions, and credits for the tax year. The K-1 forms are used by each partner to report their individual tax return.\\n3. **Self-Employment Income**: Self-employment income from a partnership is reported on Schedule C \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, which is the form used to report business income and expenses. The self-employment income includes:\\n\\t* Business income from the partnership\\n\\t* Any other self-employment income, such as freelance work or consulting fees\\n4. **Business Expenses**: Self-employed individuals can deduct business expenses related to their partnership activities on Schedule C \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. These expenses may include:\\n\\t* Business use of a home or car\\n\\t* Travel expenses\\n\\t* Equipment and supplies\\n\\t* Rent or mortgage interest\\n\\t* Utilities\\n5. **Self-Employment Tax**: Self-employed individuals must pay self-employment tax, which includes both the employee and employer portions of payroll taxes \u001b[0m\u001b[32m(\u001b[0m\u001b[32mSocial Security and Medicare taxes\u001b[0m\u001b[32m)\u001b[0m\u001b[32m. This is reported on Schedule SE \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n6. **Estimated Taxes**: Self-employed individuals are required to make estimated tax payments throughout the year if they expect to owe $1,000 or more in taxes for the year. These payments are made using Form 1040-ES.\\n7. **Quarterly Estimated Tax Payments**: The due dates for quarterly estimated tax payments are:\\n\\t* April 15th for Q1 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mJanuary 1 - March 31\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* June 15th for Q2 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mApril 1 - May 31\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* September 15th for Q3 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mJune 1 - August 31\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\t* January 15th of the following year for Q4 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mSeptember 1 - December 31\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\nIt's essential to note that self-employed individuals may need to file additional forms, such as Form 8829 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mExpenses for Business Use of Your Home\u001b[0m\u001b[32m)\u001b[0m\u001b[32m if they use a home office for business purposes.\\n\\nAs a tax preparer, I would work with the partnership and each partner to ensure accurate reporting of self-employment income from the partnership on their individual tax returns.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"As a tax preparer, I can tell you that yes, self-employed individuals can deduct their retirement plan contributions as a business expense on their tax return.\\n\\nSelf-employment income is subject to self-employment taxes, which include both the employee and employer portions of payroll taxes. However, self-employed individuals can deduct half of their net earnings from self-employment, including retirement plan contributions, as a business expense.\\n\\nThere are several types of retirement plans that qualify for deduction as a business expense:\\n\\n1. SEP-IRA \u001b[0m\u001b[32m(\u001b[0m\u001b[32mSimplified Employee Pension Individual Retirement Account\u001b[0m\u001b[32m)\u001b[0m\u001b[32m: Contributions to a SEP-IRA are deductible as a business expense.\\n2. Solo 401\u001b[0m\u001b[32m(\u001b[0m\u001b[32mk\u001b[0m\u001b[32m)\u001b[0m\u001b[32m or Individual 401\u001b[0m\u001b[32m(\u001b[0m\u001b[32mk\u001b[0m\u001b[32m)\u001b[0m\u001b[32m: Contributions to a solo 401\u001b[0m\u001b[32m(\u001b[0m\u001b[32mk\u001b[0m\u001b[32m)\u001b[0m\u001b[32m or individual 401\u001b[0m\u001b[32m(\u001b[0m\u001b[32mk\u001b[0m\u001b[32m)\u001b[0m\u001b[32m plan are deductible as a business expense.\\n3. Traditional IRA: Contributions to a traditional IRA may be deductible as a business expense, but only if the self-employed individual is not covered by another retirement plan at work.\\n4. Solo 403\u001b[0m\u001b[32m(\u001b[0m\u001b[32mb\u001b[0m\u001b[32m)\u001b[0m\u001b[32m or Thrift Savings Plan: Contributions to a solo 403\u001b[0m\u001b[32m(\u001b[0m\u001b[32mb\u001b[0m\u001b[32m)\u001b[0m\u001b[32m or thrift savings plan are deductible as a business expense.\\n\\nTo qualify for this deduction, you must meet certain requirements, such as:\\n\\n* Being self-employed and having net earnings from self-employment\\n* Making contributions to the retirement plan within the plan's contribution limits\\n* Having a valid business purpose for making the contributions \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., to save for retirement\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\nIt's essential to keep accurate records of your retirement plan contributions, including receipts, bank statements, and any other documentation that supports your deductions. You should also consult with a tax professional or financial advisor to ensure you're meeting all the requirements and taking advantage of the deductions available to you.\\n\\nKeep in mind that deducting retirement plan contributions as a business expense can impact your self-employment taxes, so it's crucial to understand how this affects your overall tax situation.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'As a tax preparer, I\\'d be happy to explain how self-employed individuals can calculate their self-employment tax on a net loss from self-employment.\\n\\nSelf-employment tax is used to fund Social Security and Medicare. As a self-employed individual, you are responsible for paying both the employee and employer portions of these taxes, which is why it\\'s called \"self-employment tax.\" The self-employment tax rate is 15.3% of your net earnings from self-employment, which includes income from freelance work, consulting, or running a business.\\n\\nTo calculate self-employment tax on a net loss from self-employment, you\\'ll need to follow these steps:\\n\\n1. Calculate your net profit or loss from self-employment: Start by calculating your total gross income from self-employment and subtract any business expenses, deductions, and credits that reduce your taxable income.\\n2. Determine your net earnings from self-employment: This is the amount of money you have left after deducting all eligible business expenses and other deductions from your gross income.\\n3. Calculate the self-employment tax: Multiply your net earnings from self-employment by 15.3% \u001b[0m\u001b[32m(\u001b[0m\u001b[32m12.4% for Social Security and 2.9% for Medicare\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\\n4. Consider any adjustments to the self-employment tax rate: If you\\'re eligible for certain deductions or credits that reduce your taxable income, such as the home office deduction or business use of your car, these may affect your self-employment tax rate.\\n\\nHere\\'s an example:\\n\\nLet\\'s say John is a freelancer who earns $50,000 in gross income from his freelance work. He has business expenses of $20,000 and other deductions that reduce his taxable income to $30,000. His net earnings from self-employment are $30,000.\\n\\nTo calculate the self-employment tax:\\n\\n1. Calculate net earnings: $30,000 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mnet earnings\u001b[0m\u001b[32m)\u001b[0m\u001b[32m - $0 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mno business use of home or car expenses in this example\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n2. Calculate self-employment tax: 15.3% of $30,000 = $4,590\\n\\nIn this example, John\\'s self-employment tax would be $4,590.\\n\\nHowever, if John has a net loss from self-employment, he can deduct the loss on his tax return and reduce his taxable income. If John has a net loss of $10,000, his new net earnings from self-employment would be:\\n\\n$30,000 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mprevious net earnings\u001b[0m\u001b[32m)\u001b[0m\u001b[32m - $10,000 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mnet loss\u001b[0m\u001b[32m)\u001b[0m\u001b[32m = $20,000\\n\\nTo calculate the self-employment tax on this reduced amount:\\n\\n1. Calculate net earnings: $20,000\\n2. Calculate self-employment tax: 15.3% of $20,000 = $3,060\\n\\nIn this example, John\\'s self-employment tax would be $3,060.\\n\\nKeep in mind that these are simplified examples and actual calculations may vary depending on individual circumstances. It\\'s always a good idea to consult with a tax professional or accountant to ensure accurate calculations and take advantage of all eligible deductions and credits.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'As a tax preparer, I can provide guidance on this topic.\\n\\nSelf-employed individuals can indeed deduct business expenses related to the production of income that is exempt from self-employment tax. However, there are some important nuances to consider:\\n\\n1. **Business income must be subject to self-employment tax**: Self-employment tax applies to net earnings from self-employment, which includes income from a trade or business. If you have income that is not subject to self-employment tax, such as interest, dividends, or capital gains, you cannot deduct business expenses related to that income.\\n2. **Business use percentage**: To qualify for the deduction, the business expense must be directly related to the production of income that is subject to self-employment tax. You can only deduct a portion of your total business expenses based on the amount of time spent working in the trade or business. This is known as the \"business use percentage.\"\\n3. **Business use percentage calculation**: To calculate the business use percentage, you\\'ll need to keep accurate records of your business and personal activities. You can use methods such as:\\n\\t* Time tracking: Record the number of hours worked on business versus personal activities.\\n\\t* Logbook or journal: Keep a log of business-related activities, including dates, times, and purposes.\\n\\t* Mileage log: If you drive for business, keep track of miles driven for business purposes.\\n4. **Deduction limits**: The IRS allows self-employed individuals to deduct business expenses up to the amount of their net earnings from self-employment. This means that if your net earnings are $100,000, and you have $50,000 in business expenses, you can only deduct up to $50,000.\\n\\nExamples of business expenses that may be deductible for income exempt from self-employment tax include:\\n\\n* Rent or mortgage interest \u001b[0m\u001b[32m(\u001b[0m\u001b[32mif used for a home office\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* Utilities \u001b[0m\u001b[32m(\u001b[0m\u001b[32melectricity, gas, water, internet\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* Office supplies\\n* Travel expenses related to business activities\\n* Meals and entertainment \u001b[0m\u001b[32m(\u001b[0m\u001b[32msubject to certain limits\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\nHowever, some expenses are not deductible, such as:\\n\\n* Personal use of your car \u001b[0m\u001b[32m(\u001b[0m\u001b[32munless you have a dedicated business vehicle\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* Home improvements or renovations\\n* Business use of personal phone or computer\\n\\nIt\\'s essential to keep accurate records and consult with a tax professional to ensure you\\'re taking advantage of the deductions you\\'re eligible for.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'As a tax preparer, I\\'d be happy to help clarify how self-employed individuals can report their income from a business with both self-employment income and income that is exempt from self-employment tax.\\n\\nSelf-employment income includes earnings from a trade or business, such as freelancing, consulting, or running a small business. However, not all self-employment income is subject to self-employment tax. Some types of income are exempt from self-employment tax, such as:\\n\\n1. Income earned by employees who are considered employees for tax purposes \u001b[0m\u001b[32m(\u001b[0m\u001b[32me.g., W-2 income\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n2. Income earned by individuals with disabilities or certain medical conditions that prevent them from working\\n3. Income earned by individuals who are retired or disabled and receiving Social Security benefits\\n\\nTo report self-employment income, the individual must complete Form 1040 and Schedule C \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, which is used to report business income and expenses. The self-employed individual will report their total net earnings from self-employment on Line 1 of Schedule C.\\n\\nHowever, if some of the self-employment income is exempt from self-employment tax, it\\'s essential to report that income separately. Here are a few scenarios:\\n\\nScenario 1: Exempt income is not subject to self-employment tax\\n\\nIf the business has both self-employment income and exempt income, the individual should report the exempt income on their tax return as ordinary income on Line 21 of Form 1040. The self-employment income will still be reported on Schedule C.\\n\\nScenario 2: Exempt income is subject to self-employment tax\\n\\nIf the business has both self-employment income and exempt income that is subject to self-employment tax, the individual should report the exempt income as \"Other Income\" on Line 21 of Form 1040. The self-employment income will still be reported on Schedule C.\\n\\nScenario 3: Exempt income is not subject to self-employment tax, but it\\'s also not ordinary income\\n\\nIf the business has both self-employment income and exempt income that are not subject to self-employment tax, the individual should report the exempt income as \"Other Income\" on Line 21 of Form 1040. The self-employment income will still be reported on Schedule C.\\n\\nIn all cases, the individual must also complete Schedule SE \u001b[0m\u001b[32m(\u001b[0m\u001b[32mForm 1040\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, which is used to calculate and pay self-employment tax. However, if some of the exempt income is not subject to self-employment tax, the individual may not need to pay self-employment tax on that amount.\\n\\nIt\\'s essential for self-employed individuals to keep accurate records of their business income and expenses to ensure they accurately report their income and claim any applicable deductions. It\\'s also recommended that they consult with a tax professional or accountant to ensure compliance with all tax laws and regulations.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m\n",
              "\u001b[2;32m│   \u001b[0m\u001b[1m]\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mscores\u001b[0m=\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[32m'braintrust::answer-similarity'\u001b[0m: \u001b[1;35mScoringResult\u001b[0m\u001b[1m(\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[33maggregated_results\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'average'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'average'\u001b[0m: \u001b[1;36m0.4899263859389534\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[33mscore_rows\u001b[0m=\u001b[1m[\u001b[0m\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5540326316427405\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6107129438872975\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6295656173500133\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6621756465647113\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.7251324334585492\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6580514616988463\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.679013668656233\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6443694159054953\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6534822247099343\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6060499995255393\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6707352238393781\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5844465262881663\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6193049787006669\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.19265334618395002\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.3475911229721721\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.37030823883470115\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.25236308267577573\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5402693248940148\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5971543063171332\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.4717556066495579\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5813241919626898\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.42594780058940307\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.3775577464216217\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5752785957156418\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.4928045325528636\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6130954353884036\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5731572219578517\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.2721622295062875\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.4909561413127072\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.43785619682763427\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.43196526476505026\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.48082666644275657\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.3871573389983647\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5141049206455494\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.15621815507500153\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.23346143409633255\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5233557444748452\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.584189246942877\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.39744129545413726\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.423957948569605\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.11441727054056215\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.49638560386493197\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.4140458125149959\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[1m]\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m)\u001b[0m\n",
              "\u001b[2;32m│   \u001b[0m\u001b[1m}\u001b[0m\n",
              "\u001b[1m)\u001b[0m\n"
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">EvaluateResponse</span><span style=\"font-weight: bold\">(</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">generations</span>=<span style=\"font-weight: bold\">[</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"The primary purpose of a W-2 form, also known as a Wage and Tax Statement, is to report an employee's income earned from their employer to the Internal Revenue Service (IRS) for federal income tax purposes. The W-2 form is used by employers to provide employees with a summary of their earnings and taxes withheld from their paychecks throughout the year.\\n\\nThe W-2 form typically includes information such as:\\n\\n* Employee's name, address, and Social Security number\\n* Employer's name, address, and Employer Identification Number (EIN)\\n* Gross wages earned during the tax year\\n* Federal income tax withheld\\n* State and local taxes withheld (if applicable)\\n* Other deductions and credits claimed by the employee\\n\\nThe primary purpose of a W-2 form is to:\\n\\n1. Report an employee's income to the IRS: The W-2 form serves as proof of income earned by employees, which is used by the IRS to determine how much tax should be withheld from future paychecks.\\n2. Provide information for tax withholding: The W-2 form helps employers calculate and withhold the correct amount of federal income tax, Social Security tax, and Medicare tax from an employee's wages.\\n3. Allow employees to file their tax returns accurately: By providing a summary of their earnings and taxes withheld, the W-2 form enables employees to complete their tax returns accurately and claim any additional credits or deductions they may be eligible for.\\n\\nOverall, the W-2 form plays a critical role in ensuring that employers comply with federal income tax laws and regulations, while also helping employees manage their tax obligations and take advantage of available credits and deductions.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I'd be happy to explain how W-2 income affects an individual's tax liability.\\n\\nW-2 income refers to the wages and salaries earned by an employee from their employer. The amount of W-2 income reported on an individual's W-2 form is used to determine their taxable income for the year. Here are some ways in which W-2 income can affect an individual's tax liability:\\n\\n1. **Taxable Income**: W-2 income is considered taxable income, meaning it is subject to federal and state income taxes. The amount of W-2 income reported on the form will be used to calculate the individual's total taxable income for the year.\\n2. **Tax Brackets**: W-2 income falls into one of several tax brackets, which determine the tax rate applied to that income. As an individual earns more W-2 income, they may move up or down a tax bracket, affecting their overall tax liability.\\n3. **Deductions and Credits**: Depending on the individual's circumstances, they may be eligible for deductions and credits that can reduce their taxable income. For example, if an individual has health insurance premiums through their employer, they may be able to claim a deduction for those premiums. Similarly, if they have children or are married, they may be eligible for credits like the Earned Income Tax Credit (EITC) or Child Tax Credit.\\n4. **Self-Employment Taxes**: If an individual has self-employment income reported on their W-2 form, it will also be subject to self-employment taxes. This can increase their overall tax liability, as self-employment taxes are typically 15.3% of net earnings from self-employment (12.4% for Social Security and 2.9% for Medicare).\\n5. **Tax Credits**: Some W-2 income may qualify for tax credits, such as the Child Tax Credit or Education Credits. These credits can directly reduce an individual's tax liability.\\n6. **Tax Withholding**: Employers are required to withhold federal income taxes from employee wages and pay them over to the government on behalf of their employees. This withholding reduces the amount of taxes owed when filing a tax return, but it also means that some taxes may be withheld too much, resulting in a larger refund or less tax liability.\\n7. **Tax Liabilities**: If an individual has W-2 income from multiple sources (e.g., employment and self-employment), their overall tax liability will depend on the combined amount of taxable income.\\n\\nTo minimize tax liability, individuals with W-2 income should consider factors such as:\\n\\n* Taking advantage of deductions and credits available to them\\n* Adjusting withholding amounts through payroll adjustments or estimated tax payments\\n* Considering retirement savings options, like 401(k) or IRA contributions\\n* Reviewing their overall financial situation to identify areas for tax optimization\\n\\nAs a tax preparer, I would work with clients to help them navigate these factors and optimize their W-2 income to minimize their tax liability.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I can tell you that W-2 income is generally considered taxable income and cannot be adjusted for tax purposes.\\n\\nA W-2 form is used to report an employee's wages and taxes withheld from their paycheck. The income reported on the W-2 is considered taxable income and must be included in the taxpayer's gross income for tax purposes.\\n\\nHowever, there are some exceptions and potential adjustments that can be made to W-2 income for tax purposes:\\n\\n1. **Corrected W-2s**: If an employer makes a mistake on the W-2 form, such as underreporting or overpaying taxes withheld, they may issue a corrected W-2 to the employee. In this case, the corrected amount can be adjusted on the taxpayer's return.\\n2. **Tax credits and deductions**: Taxpayers may be eligible for tax credits or deductions that reduce their taxable income, such as the Earned Income Tax Credit (EITC), Child Tax Credit, or education credits. These credits and deductions can reduce the amount of W-2 income subject to taxation.\\n3. **Self-employment income**: If an employee has self-employment income reported on a 1099-MISC form, they may be able to deduct business expenses related to that income on their tax return. This can potentially reduce their taxable income from the W-2 income.\\n4. **Tax law changes**: Changes in tax laws or regulations can affect how W-2 income is taxed. For example, if a new tax law reduces the tax rate for certain types of income, it may be possible to adjust the taxpayer's return to reflect this change.\\n\\nHowever, these exceptions and adjustments are subject to specific rules and requirements, and taxpayers should consult with a tax professional or the IRS to determine the best course of action.\\n\\nIn general, W-2 income is considered taxable income and cannot be adjusted for tax purposes without proper documentation and approval from the employer or the IRS.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I can tell you that the Internal Revenue Service (IRS) uses various methods to verify W-2 income. Here are some of the ways they verify W-2 income:\\n\\n1. **Employer Reporting**: The most common method is through employer reporting. Employers are required to provide employees with a Form W-2, Wage and Tax Statement, by January 31st of each year, showing their wages, taxes withheld, and other relevant information. This form serves as proof of employment income.\\n2. **Form 1099-MISC**: If an individual receives freelance or contract work, they may receive a Form 1099-MISC, Miscellaneous Income, from the payer. This form reports non-employee compensation, such as freelance work, rent, and royalties.\\n3. **Bank Statements**: The IRS can review bank statements to verify income reported on W-2s. They may request bank statements to confirm that the income reported on the W-2 is accurate.\\n4. **Employer Verification Letters**: In some cases, the IRS may request a letter from the employer verifying the employee's income and employment status.\\n5. **Taxpayer Identification Number (TIN) Verification**: The IRS can verify an individual's TIN through various sources, including:\\n\\t* Social Security Administration (SSA)\\n\\t* Internal Revenue Service (IRS)\\n\\t* State tax agencies\\n\\t* Other government agencies\\n6. **Address Verification**: The IRS may request verification of an individual's address to ensure that the W-2 is being sent to the correct address.\\n7. **Audit Trails**: Employers are required to maintain records of employee wages and taxes withheld for at least three years. These records can be reviewed by the IRS during an audit.\\n\\nTo verify W-2 income, the IRS may use various tools and resources, including:\\n\\n1. The Electronic Federal Tax Payment System (EFTPS)\\n2. The IRS Data Retrieval Tool\\n3. The IRS's online database of tax returns and transcripts\\n\\nIt's worth noting that the IRS can request additional documentation or information to verify W-2 income if they suspect any discrepancies or errors on the return. As a tax preparer, it's essential to ensure that all required documentation is accurate and complete to avoid any potential issues with the IRS.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I'd be happy to explain how pre-tax deductions can impact W-2 income.\\n\\nPre-tax deductions, also known as pre-tax contributions or pre-tax withholdings, refer to amounts withheld from an employee's paycheck before taxes are taken out. These deductions are typically made through payroll deductions, such as 401(k), 403(b), Health Savings Account (HSA), Flexible Spending Arrangements (FSA), or other qualified retirement plans.\\n\\nWhen it comes to W-2 income, pre-tax deductions can affect the amount of taxable income reported on your tax return. Here's how:\\n\\n1. **Reduced Gross Income**: Pre-tax deductions are subtracted from your gross income before taxes are taken out. This means that the amount of money you take home each paycheck is lower than your gross income.\\n2. **Lower Taxable Income**: Since pre-tax deductions reduce your gross income, they also reduce your taxable income. As a result, your tax liability will be lower, and you may receive a larger refund or pay less in taxes throughout the year.\\n3. **Tax-Deferred Growth**: Pre-tax contributions to retirement plans like 401(k) or 403(b) grow tax-deferred, meaning they are not subject to income tax until withdrawal. This can help your savings grow faster over time.\\n\\nTo illustrate this concept, let's consider an example:\\n\\nSuppose you earn $50,000 per year and contribute $5,000 to a 401(k) plan through payroll deductions. Your gross income would be reduced by $5,000, making your take-home pay $45,000. Since the contribution is made before taxes are taken out, it reduces your taxable income.\\n\\nOn your tax return, you'll report your adjusted gross income (AGI), which includes the pre-tax contributions to your 401(k) plan. This can result in a lower AGI and potentially lower taxes owed or a larger refund.\\n\\nKeep in mind that while pre-tax deductions reduce your taxable income, they also reduce your take-home pay. It's essential to consider how these deductions impact your overall financial situation and adjust your budget accordingly.\\n\\nAs a tax preparer, I always advise clients to review their W-2 income and pre-tax deductions to ensure they're taking advantage of available tax savings opportunities while maintaining a healthy balance between saving for retirement and enjoying their hard-earned money.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I can tell you that yes, it is possible for an individual to receive W-2 income from multiple employers and have those amounts reported on separate W-2 forms.\\n\\nIn general, the IRS requires each employer to report all wages, tips, and other compensation paid to an employee on a single W-2 form. However, there are some exceptions and special circumstances that may result in multiple W-2 forms being issued:\\n\\n1. **Multiple jobs**: If you have multiple jobs or positions with different employers during the same tax year, each employer will issue a separate W-2 form showing their portion of your total income.\\n2. **Self-employment income**: If you are self-employed and earn income from a business or freelance work, you may receive a 1099-MISC form (not a W-2) from yourself as the business owner. However, if you also have other employment income reported on a W-2, both forms will be issued.\\n3. **Gig economy workers**: If you work through platforms like Uber, Lyft, or Airbnb, you may receive multiple 1099-K forms (not W-2s) from these companies, as they are considered independent contractors rather than employees.\\n4. **Government employment**: Federal, state, and local government employees typically receive a single W-2 form showing their total compensation for the year.\\n5. **Retirement plan distributions**: If you receive retirement plan distributions (e.g., 401(k), IRA) from multiple sources, each plan may issue separate W-2 forms or 1099-R forms.\\n\\nWhen an individual receives income from multiple sources, it's essential to report all of these amounts on their tax return. The IRS requires that you combine the income from all sources and report it on your tax return, regardless of whether it was reported on a single W-2 form or multiple ones.\\n\\nAs a tax preparer, I would ensure that my clients accurately report all income from multiple sources on their tax returns to avoid any potential issues with the IRS.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I'd be happy to explain how W-2 income is affected by tax credits.\\n\\nW-2 income refers to the wages and salaries reported on your Form W-2, which you receive from your employer at the end of each year. Tax credits are deductions or reductions in the amount of taxes you owe, rather than a direct reduction in your taxable income.\\n\\nHere's how W-2 income is affected by tax credits:\\n\\n1. **Taxable income**: Your W-2 income is considered taxable income and is subject to federal income tax withholding.\\n2. **Tax credits vs. deductions**: Tax credits are different from deductions. Deductions reduce the amount of income that is subject to taxation, while credits directly reduce the amount of taxes you owe.\\n3. **Tax credits can reduce or eliminate taxes owed**: If you have eligible tax credits, such as the Earned Income Tax Credit (EITC), Child Tax Credit, or Education Credits, these credits can reduce your taxable income and, in some cases, even result in a refund if the credit exceeds the amount of taxes owed.\\n4. **Tax credits may not directly affect W-2 income**: However, tax credits can indirectly affect your W-2 income by reducing the amount of taxes withheld from your paycheck. For example, if you have a child and are eligible for the Child Tax Credit, your employer may reduce the amount of federal income tax withheld from your paychecks to reflect the credit.\\n5. **Tax credits can increase your refund**: If you have multiple tax credits that exceed your tax liability, you may receive a larger refund than you would if you didn't have any credits.\\n\\nTo illustrate this, let's consider an example:\\n\\nSuppose John has W-2 income of $50,000 and is eligible for the Earned Income Tax Credit (EITC) worth $5,000. His total tax liability before credits would be approximately 20% of his taxable income ($10,000). With the EITC credit, his new tax liability would be reduced to $5,000, resulting in a larger refund.\\n\\nIn summary, W-2 income is subject to taxation and withholding, but tax credits can reduce your taxable income or directly reduce the amount of taxes owed. Tax credits can also indirectly affect your W-2 income by reducing the amount of taxes withheld from your paycheck.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'As a tax preparer, I\\'d be happy to explain how W-2 income affects the Alternative Minimum Tax (AMT).\\n\\nThe Alternative Minimum Tax (AMT) is a provision in the US tax code that requires individuals and businesses to pay taxes at a minimum rate of 26% on certain types of income. The AMT was created to ensure that taxpayers don\\'t benefit from tax loopholes and deductions that allow them to avoid paying their \"fair share\" of taxes.\\n\\nW-2 income, which represents the income earned by employees, is subject to the AMT if it exceeds certain thresholds. Here\\'s how W-2 income affects the AMT:\\n\\n1. **AMT Exclusion**: The first $80,250 of W-2 income (for tax year 2022) is exempt from the AMT. This means that if your W-2 income is below this threshold, you won\\'t be subject to the AMT.\\n2. **AMT Taxable Income**: If your W-2 income exceeds the $80,250 threshold, it\\'s considered taxable income for AMT purposes. The amount above the threshold is then used to calculate the AMT liability.\\n3. **AMT Deductions and Credits**: Certain deductions and credits can reduce the AMT liability. These include:\\n\\t* Personal exemptions (standard deduction or itemized deductions)\\n\\t* State and local taxes\\n\\t* Mortgage interest and property taxes\\n\\t* Charitable contributions\\n\\t* Medical expenses\\n4. **AMT Exemptions**: Some types of income are exempt from the AMT, including:\\n\\t* Interest on certain types of bonds (e.g., municipal bonds)\\n\\t* Dividend income from qualified dividends\\n\\t* Capital gains from investments\\n\\nTo determine if you\\'re subject to the AMT, your W-2 income is compared to the AMT exemption amount. If your W-2 income exceeds the exemption amount, you\\'ll need to complete Form 6251 (Alternative Minimum Tax - Individual) and calculate your AMT liability.\\n\\nKeep in mind that the AMT can be complex, and there are many factors that can affect your eligibility for exemptions and deductions. As a tax preparer, I would work with you to ensure you\\'re taking advantage of all eligible deductions and credits to minimize your AMT liability.\\n\\nDo you have any specific questions about how W-2 income affects the AMT or would you like me to elaborate on any of these points?'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"The Tax Cuts and Jobs Act (TCJA) made significant changes to the way W-2 income is taxed, particularly for employees who receive a W-2 form from their employer. Here are some key ways in which the TCJA affects W-2 income:\\n\\n1. **Standard Deduction Increase**: The TCJA increased the standard deduction for single filers from $6,350 to $12,000 and for joint filers from $12,700 to $24,400. This means that more employees may not need to itemize their deductions on their tax return, which can reduce their W-2 income.\\n2. **State and Local Taxes (SALT) Limitation**: The TCJA limited the deduction for state and local taxes (SALT) to $10,000 per year. This means that if an employee's SALT deduction exceeds $10,000, they may not be able to deduct it on their tax return.\\n3. **Child Tax Credit**: The TCJA increased the child tax credit from $1,000 to $2,000 per child under age 17 (or $3,000 for one qualifying child under age 17 if both parents are claimed as dependents). This can result in a larger W-2 income for employees with children.\\n4. **Earned Income Tax Credit (EITC)**: The TCJA expanded the EITC to include more low-to-moderate-income workers, which may increase their W-2 income due to the increased credit amount.\\n5. **Health Savings Account (HSA) Contributions**: The TCJA allowed employees to contribute up to $3,550 to a Health Savings Account (HSA) in 2019 and 2020, an increase from $3,300 in previous years. This can result in a larger W-2 income for employees who participate in an HSA.\\n6. **Retirement Plan Contributions**: The TCJA increased the annual contribution limits for 401(k), 403(b), and other retirement plans. This may result in a larger W-2 income for employees who contribute to these plans.\\n\\nHowever, it's essential to note that not all W-2 income is affected by the TCJA. For example:\\n\\n* **Self-Employment Income**: Self-employed individuals are not subject to the same tax changes as employees with W-2 income.\\n* **Health Insurance Premiums**: The TCJA did not change the way health insurance premiums are taxed, so this will not affect W-2 income.\\n\\nIt's always a good idea for employees to consult with their employer or a tax professional to understand how the TCJA affects their specific situation and to ensure they're taking advantage of any available tax savings opportunities.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"The Net Investment Income Tax (NIIT) is a provision in the Tax Cuts and Jobs Act (TCJA) that was enacted in 2017. It applies to certain types of investment income, including interest, dividends, capital gains, and qualified dividend income.\\n\\nW-2 income, on the other hand, is ordinary income earned from employment, such as wages, salaries, tips, and other forms of compensation received by an individual for their work.\\n\\nThe impact of W-2 income on the Net Investment Income Tax (NIIT) is that it does not directly affect the NIIT. The NIIT only applies to investment income, which includes:\\n\\n* Interest income from bonds, CDs, and other debt instruments\\n* Dividend income from stocks and mutual funds\\n* Capital gains from the sale of securities\\n* Qualified dividend income from certain types of investments\\n\\nW-2 income is considered ordinary income and is subject to regular income tax rates, not the NIIT. However, if you have investment income that is subject to the NIIT, your W-2 income may be used to offset some or all of the excess investment income.\\n\\nFor example, let's say you have a W-2 income of $50,000 and also have $20,000 in interest income from bonds. If your total taxable income exceeds the standard deduction amount for your filing status, you would pay tax on both the W-2 income and the interest income. However, if your investment income is subject to the NIIT, it may reduce your overall tax liability.\\n\\nTo illustrate this, let's say your W-2 income is $50,000 and your total taxable income is $60,000 (after deductions). If you have $20,000 in interest income that is subject to the NIIT, your effective tax rate on the investment income would be 3.8% (the top marginal rate for single filers with modified adjusted gross income above $200,000 or $250,000 for joint filers). In this scenario, you would pay 3.8% of $20,000 in interest income, which is $760.\\n\\nIn contrast, your W-2 income would be taxed at the regular tax rates, which might be 24% (the top marginal rate for single filers with taxable income above $80,000). In this scenario, you would pay 24% of $50,000 in W-2 income, which is $12,000.\\n\\nIn summary, while W-2 income does not directly impact the Net Investment Income Tax (NIIT), it can affect your overall tax liability if you have significant investment income that is subject to the NIIT.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I'd be happy to explain how W-2 income is affected by the Affordable Care Act (ACA).\\n\\nThe ACA, also known as Obamacare, has had a significant impact on W-2 income in several ways:\\n\\n1. **Health Insurance Premium Tax Credit**: The ACA introduced a premium tax credit for individuals and families who purchase health insurance through the Health Insurance Marketplace or their employer-sponsored plan. This credit can reduce the amount of taxes owed on W-2 income.\\n2. **Health Savings Account (HSA) contributions**: If you have a high-deductible health plan, you may be eligible to contribute to an HSA. Contributions to HSAs are tax-deductible and can be used for qualified medical expenses. The ACA has expanded the types of expenses that qualify for HSA funding.\\n3. **Dependent care credits**: The ACA introduced new dependent care credits for families with qualifying children under age 13 or disabled individuals who need care. These credits can reduce W-2 income subject to self-employment tax.\\n4. **Medicare taxes**: The ACA has changed the way Medicare taxes are applied to W-2 income. For employees, Medicare taxes are now split between the employee and employer, with the employer paying 1.45% of wages up to $200,000 (previously $110,100) and 0.45% above that amount.\\n5. **Health insurance premiums**: The ACA has required employers to offer health insurance coverage to their employees or face penalties. This means that many W-2 income earners may have had health insurance coverage through their employer, which can impact their tax obligations.\\n\\nTo take advantage of these benefits, individuals and families must meet certain eligibility requirements, such as:\\n\\n* Being under age 65\\n* Not being eligible for Medicare\\n* Having a qualifying child or dependent\\n* Meeting income limits (varies by family size and filing status)\\n\\nAs a tax preparer, I would need to review each client's individual circumstances to determine how the ACA affects their W-2 income. This may involve reviewing their health insurance coverage, HSA contributions, dependent care credits, Medicare taxes, and other factors to ensure they are taking advantage of all eligible benefits.\\n\\nKeep in mind that tax laws and regulations can change frequently, so it's essential to stay informed about any updates or changes that may affect W-2 income.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'As a tax preparer, I\\'d be happy to explain how W-2 income affects self-employment tax.\\n\\nSelf-employment tax is a type of tax that is used to fund Social Security and Medicare. It\\'s typically paid by individuals who are self-employed or have a side hustle. The good news is that you don\\'t pay self-employment tax on your W-2 income, but there are some nuances to consider.\\n\\nHere\\'s the key point: if you receive a W-2 from an employer, you\\'re not subject to self-employment tax on that income because it\\'s considered \"earned income\" rather than self-employment income. Earned income is income earned through employment, such as wages or salaries.\\n\\nHowever, there are some exceptions and considerations:\\n\\n1. **Self-Employment Tax on Business Income**: If you have a side hustle or business, you\\'re subject to self-employment tax on the net earnings from that business. This includes income from freelancing, consulting, renting out a room on Airbnb, or any other type of business activity.\\n2. **Net Earnings from Self-Employment**: To calculate self-employment tax, you need to determine your net earnings from self-employment. This is calculated by subtracting business expenses and deductions from your gross income. If your net earnings are $400 or more, you\\'re subject to self-employment tax.\\n3. **Self-Employment Tax Rate**: The self-employment tax rate is 15.3% of your net earnings from self-employment, which includes both the employee and employer portions of Social Security and Medicare taxes. This rate applies to your net earnings from self-employment, not your W-2 income.\\n4. **Self-Employment Tax Deduction**: You can deduct half of your self-employment tax as a business expense on Schedule C (Form 1040). This can help reduce your taxable income and lower your overall tax liability.\\n\\nTo illustrate this, let\\'s say you have a side hustle that generates $50,000 in net earnings from self-employment. Your self-employment tax would be:\\n\\n$50,000 x 15.3% = $7,650\\n\\nYou can deduct half of this amount as a business expense on Schedule C, which reduces your taxable income and lowers your overall tax liability.\\n\\nIn summary, W-2 income is not subject to self-employment tax because it\\'s considered earned income, but if you have a side hustle or business, you\\'re subject to self-employment tax on the net earnings from that activity.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I'd be happy to explain how W-2 income is affected by the Foreign Earned Income Exclusion.\\n\\nThe Foreign Earned Income Exclusion (FEIE) is a tax benefit that allows certain individuals to exclude up to a certain amount of foreign-earned income from their U.S. taxable income. This exclusion can significantly reduce or even eliminate the amount of taxes owed on foreign-earned income, making it an attractive option for expats and international workers.\\n\\nHere's how W-2 income is affected by the FEIE:\\n\\n1. **Eligibility**: To qualify for the FEIE, you must have earned income from a foreign employer while living outside the United States for at least 330 full days in any 12-month period (or 183 days if married to a U.S. citizen or resident).\\n2. **Exclusion amount**: The FEIE allows you to exclude up to $105,900 of foreign-earned income from your U.S. taxable income for tax year 2023. For tax years prior to 2018, the exclusion amount was $100,800.\\n3. **W-2 reporting**: When filing a U.S. tax return (Form 1040), you'll report your W-2 income on Line 21 of Form 1040. However, if you qualify for the FEIE, you can exclude this amount from your U.S. taxable income by completing Form 2555 and attaching it to your tax return.\\n4. **Foreign earned income**: The FEIE applies only to foreign-earned income, which includes:\\n\\t* Salary or wages\\n\\t* Other compensation (e.g., bonuses, commissions)\\n\\t* Rent or royalty income\\n\\t* Interest on foreign debt\\n\\t* Dividend income from a foreign corporation\\n5. **Tax implications**: If you qualify for the FEIE, your W-2 income will be excluded from U.S. taxation, and you won't owe federal income tax on that amount. However, you may still owe state or local taxes on this income.\\n6. **Reporting requirements**: You must file Form 2555 with your tax return to claim the FEIE exclusion. This form requires you to provide documentation of your foreign work experience and income.\\n\\nIt's essential to note that the FEIE has some limitations and nuances, such as:\\n\\n* The exclusion amount may be reduced if you have U.S. source income (e.g., dividends or interest from U.S.-sourced investments).\\n* You can only exclude foreign-earned income earned while living outside the United States.\\n* If you're married to a U.S. citizen or resident, your spouse's foreign-earned income is not subject to the FEIE.\\n\\nAs a tax preparer, I recommend that individuals with W-2 income from abroad consult with me to determine if they qualify for the Foreign Earned Income Exclusion and to ensure accurate reporting on their tax return.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I can tell you that a 1099-MISC form is used to report miscellaneous income that is not subject to withholding. The types of income typically reported on a 1099-MISC form include:\\n\\n1. Freelance work or independent contractor income: This includes income earned by freelancers, consultants, and independent contractors for services performed for clients.\\n2. Rent from real estate investments: Income from renting out properties, such as rental income from apartments, houses, or commercial buildings.\\n3. Royalties: Income received from the sale of intellectual property, such as music, art, literature, or inventions.\\n4. Prizes and awards: Winnings from contests, sweepstakes, or other games that are not subject to withholding.\\n5. Other miscellaneous income: This can include income from sales of goods or services that are not subject to withholding, such as bartering or commission-based income.\\n\\nThe 1099-MISC form is used by the IRS to report these types of income because it is not subject to withholding, meaning that no taxes were withheld at the source. As a result, the recipient of the income must report this income on their tax return and pay any applicable taxes, including self-employment tax.\\n\\nIt's worth noting that not all 1099-MISC forms are created equal. There are different types of 1099 forms, such as:\\n\\n* 1099-MISC: Used for miscellaneous income\\n* 1099-K: Used for payment card and third-party network transactions\\n* 1099-INT: Used for interest income\\n* 1099-DIV: Used for dividend income\\n\\nAs a tax preparer, I would work with clients to ensure they accurately report all types of income on their tax return, including those reported on a 1099-MISC form.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I can tell you that the IRS requires a 1099-MISC (Miscellaneous Income) form to be issued to independent contractors who have earned more than $600 in gross payments from a single payer during the calendar year.\\n\\nThe IRS defines an independent contractor as someone who is not considered an employee and is paid on a contract basis. This includes freelancers, consultants, independent contractors, and other self-employed individuals.\\n\\nTo qualify for a 1099-MISC form, the following conditions must be met:\\n\\n1. The payer must have paid more than $600 in gross payments to the same individual during the calendar year.\\n2. The payment is not subject to withholding (e.g., no taxes are withheld).\\n3. The payment is made for services performed as an independent contractor.\\n\\nExamples of individuals who may receive a 1099-MISC form include:\\n\\n* Freelance writers, editors, and designers\\n* Independent contractors for construction or consulting work\\n* Self-employed artists, musicians, and performers\\n* Independent contractors for IT services\\n* Freelance photographers and videographers\\n\\nThe payer is responsible for issuing a 1099-MISC form to independent contractors by January 31st of each year, showing the amount paid to them during the previous tax year. The form must be sent to the contractor's address as it appears on file with the IRS.\\n\\nIt's worth noting that some payments may not require a 1099-MISC form, such as:\\n\\n* Payments made through a third-party payment service (e.g., PayPal)\\n* Payments made for services performed by an employee or an employee of the payer\\n* Payments made to a business entity (e.g., S corporation, partnership) rather than an individual\\n\\nAs a tax preparer, I would advise clients who receive 1099-MISC forms to report these payments on their tax return and pay any applicable taxes due.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I'd be happy to explain how self-employed individuals can report their business expenses on their tax return.\\n\\nSelf-employed individuals who have a business or side hustle often face unique challenges when it comes to reporting their expenses. Here's a step-by-step guide on how they can report their business expenses:\\n\\n1. **Keep accurate records**: Self-employed individuals must keep detailed and organized records of all business-related expenses, including receipts, invoices, bank statements, and credit card statements. These records should be kept for at least three years in case of an audit.\\n2. **Categorize expenses**: Business expenses can be categorized into different types, such as:\\n\\t* Operating expenses (e.g., rent, utilities, equipment, supplies)\\n\\t* Travel expenses\\n\\t* Home office expenses (if a dedicated space is used for business purposes)\\n\\t* Advertising and marketing expenses\\n\\t* Professional fees (e.g., lawyer, accountant, consultant)\\n3. **Complete Form 1040**: Self-employed individuals report their business income and expenses on Schedule C (Form 1040), which is the form used to report net profit or loss from a business.\\n4. **Calculate business use percentage**: If you have a home office, you may be able to deduct a portion of your rent or mortgage interest as a business expense using Form 8829 (Expenses for Business Use of Your Home). You'll need to calculate the business use percentage by dividing the square footage of the dedicated space used for business purposes by the total square footage of the home.\\n5. **Complete Schedule C**: On Schedule C, you'll report your business income and expenses, including:\\n\\t* Gross receipts\\n\\t* Cost of goods sold (if applicable)\\n\\t* Operating expenses (e.g., rent, utilities, supplies)\\n\\t* Travel expenses\\n\\t* Home office expenses (if applicable)\\n6. **Calculate net profit or loss**: Calculate the net profit or loss from your business by subtracting total expenses from gross receipts.\\n7. **Complete Form 1040**: Report your net profit or loss on Line 21 of Form 1040.\\n8. **Claim deductions**: Claim deductions for eligible business expenses, such as:\\n\\t* Business use percentage of home office expenses (Form 8829)\\n\\t* Travel expenses (Form 2106)\\n\\t* Professional fees\\n\\t* Advertising and marketing expenses\\n9. **Keep records**: Keep all supporting documentation, including receipts, invoices, and bank statements, to support your deductions.\\n\\nSome additional tips:\\n\\n* Consult with a tax professional or accountant if you're unsure about any aspect of reporting business expenses.\\n* Consider using accounting software or apps to help track and organize your business expenses.\\n* Be aware that the IRS has specific rules and regulations regarding business expense deductions, so it's essential to follow these guidelines carefully.\\n\\nBy following these steps and keeping accurate records, self-employed individuals can ensure they're taking advantage of all eligible business expense deductions on their tax return.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"The self-employment tax rate for net earnings from self-employment is 15.3% of your net earnings from self-employment, which includes income from freelance work, consulting, or running a business.\\n\\nThis rate consists of two parts:\\n\\n1. The employee portion: 12.4% (6.2% for Social Security and 6.2% for Medicare)\\n2. The employer portion: 2.9% (1.45% for Social Security and 1.45% for Medicare)\\n\\nSince you are self-employed, you are both the employee and the employer, so you pay both parts of the tax.\\n\\nTo calculate your self-employment tax, you'll need to add the employee portion and the employer portion together:\\n\\n12.4% (employee portion) + 2.9% (employer portion) = 15.3%\\n\\nSo, for every dollar you earn from self-employment, you pay 15.3% in self-employment tax.\\n\\nKeep in mind that this rate applies to your net earnings from self-employment, which is your total income minus any business expenses and deductions.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I'd be happy to explain how self-employed individuals can calculate their self-employment tax deduction.\\n\\nThe self-employment tax is used to fund Social Security and Medicare taxes for self-employed individuals. The amount of self-employment tax you pay depends on your net earnings from self-employment, which includes income from a business or freelance work.\\n\\nHere's the step-by-step process to calculate self-employment tax:\\n\\n1. **Calculate your net earnings from self-employment**: Start by calculating your total gross income from self-employment, including income from freelancing, consulting, or running a small business.\\n2. **Deduct business expenses**: Subtract business expenses related to your self-employment from your gross income. This includes expenses such as:\\n\\t* Business use of your home (home office deduction)\\n\\t* Travel expenses\\n\\t* Equipment and supplies\\n\\t* Rent or mortgage interest\\n\\t* Utilities\\n\\t* Insurance premiums\\n\\t* Advertising and marketing expenses\\n\\t* Professional fees (e.g., lawyer, accountant, etc.)\\n3. **Calculate your net earnings from self-employment**: Subtract the business expenses from your gross income to get your net earnings from self-employment.\\n4. **Determine your self-employment tax rate**: The self-employment tax rate is 15.3% of your net earnings from self-employment, which includes:\\n\\t* 12.4% for Social Security (old-age, survivors, and disability insurance)\\n\\t* 2.9% for Medicare (hospital insurance)\\n5. **Calculate your self-employment tax**: Multiply your net earnings from self-employment by the self-employment tax rate (15.3%) to calculate your self-employment tax.\\n6. **Optional: Calculate the self-employment tax deduction**: If you're eligible, you may be able to deduct half of your self-employment tax as a business expense on Schedule C (Form 1040). This can help reduce your taxable income and lower your overall tax liability.\\n\\nExample:\\n\\nLet's say John is a freelancer who earns $50,000 in gross income from his freelance work. He has business expenses of $10,000, which includes home office expenses, travel expenses, equipment, and supplies.\\n\\n1. Net earnings from self-employment: $50,000 - $10,000 = $40,000\\n2. Self-employment tax rate: 15.3% (12.4% for Social Security + 2.9% for Medicare)\\n3. Self-employment tax: $40,000 x 15.3% = $6,120\\n4. Optional self-employment tax deduction: John may be able to deduct half of the self-employment tax ($6,120 / 2) as a business expense on Schedule C.\\n\\nKeep in mind that this is just an example and actual calculations may vary depending on individual circumstances. It's always best to consult with a tax professional or accountant to ensure accurate calculations and maximize your deductions.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I can tell you that yes, self-employed individuals can deduct business expenses related to their home office. This is known as the Home Office Deduction.\\n\\nThe IRS allows self-employed individuals to deduct a portion of their rent or mortgage interest and utilities as a business expense if they use a dedicated space in their home regularly and exclusively for business purposes. The amount of the deduction depends on the square footage of the home office used for business, which can be calculated using one of two methods:\\n\\n1. **Simplified Option**: This method allows self-employed individuals to deduct $5 per square foot of home office space, up to a maximum of $1,500.\\n2. **Actual Expenses Method**: This method requires calculating the actual expenses related to the home office, such as rent or mortgage interest, utilities, insurance, and maintenance costs.\\n\\nTo qualify for the Home Office Deduction, the following conditions must be met:\\n\\n* The space used for business must be a regular and exclusive use of the home.\\n* The space must be used regularly and exclusively for business purposes (e.g., no personal activities).\\n* The space must be used in connection with the conduct of a trade or business.\\n\\nSome examples of eligible expenses that can be deducted as part of the Home Office Deduction include:\\n\\n* Rent or mortgage interest\\n* Utilities (electricity, gas, water, etc.)\\n* Insurance premiums\\n* Maintenance and repairs\\n* Depreciation on home office equipment\\n\\nHowever, some expenses are not eligible for deduction, such as:\\n\\n* Personal use of the space (e.g., a home office that is also used for personal activities like reading or watching TV)\\n* Improvements made to the home that benefit both business and personal use (e.g., installing a new kitchen sink)\\n\\nIt's essential to keep accurate records of your home office expenses, including:\\n\\n* A log or calendar showing the dates and hours spent working from home\\n* Photos or measurements of the dedicated workspace\\n* Invoices and receipts for rent, utilities, insurance, and other expenses\\n\\nConsult with a tax professional, like myself, to ensure you're taking advantage of this valuable deduction and following the correct procedures.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I'd be happy to explain how self-employed individuals can calculate the business use percentage of their home.\\n\\nThe IRS allows self-employed individuals to deduct a portion of their rent or mortgage interest and utilities as a business expense if they use a dedicated space in their home regularly and exclusively for business. To calculate this deduction, you'll need to determine the business use percentage of your home.\\n\\nHere's a step-by-step process:\\n\\n1. **Determine the total square footage of your home**: Measure the total square footage of your home, including any additional living areas that are used for business.\\n2. **Measure the dedicated business space**: Measure the square footage of the area you use regularly and exclusively for business. This could be a home office, studio, or any other space where you conduct business activities.\\n3. **Calculate the total square footage ratio**: Divide the business space by the total home square footage to get the percentage of your home used for business.\\n\\nFor example:\\n\\n* Total home square footage: 1,500 sqft\\n* Business space (home office): 200 sqft\\n\\nBusiness use percentage = (200 sqft / 1,500 sqft) x 100% = 13.33%\\n\\nThis means that 13.33% of your home is used for business.\\n\\n4. **Determine the actual expenses**: Calculate the total rent or mortgage interest and utilities you pay on your home, including any additional expenses like property taxes, insurance, and maintenance.\\n5. **Calculate the business use percentage of expenses**: Multiply the total expenses by the business use percentage calculated in step 3.\\n\\nFor example:\\n\\n* Total rent/mortgage interest: $1,500 per month\\n* Business use percentage: 13.33%\\n\\nBusiness use percentage of expenses = $1,500 x 0.1333 (13.33%) = $200\\n\\nThis means that you can deduct $200 from your business income as a home office expense.\\n\\n**Important notes:**\\n\\n* You must have a dedicated space for business and use it regularly and exclusively for business.\\n* The business use percentage should be reasonable and not excessive, as the IRS may scrutinize claims with high percentages.\\n* Keep accurate records of your expenses, including receipts, invoices, and photos of your home office, to support your calculation.\\n\\nRemember to consult with a tax professional or accountant if you're unsure about how to calculate the business use percentage of your home. They can help ensure you're taking advantage of all eligible deductions and following IRS guidelines.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"The Tax Cuts and Jobs Act (TCJA) made significant changes to the home office deduction, which was previously known as the home office expense deduction or the business use percentage method. Here are some key impacts of the TCJA on the home office deduction:\\n\\n1. **Simplified Option**: The TCJA introduced a simplified option for self-employed individuals and sole proprietors to deduct a fixed amount of $5 per square foot of home office space, up to a maximum of $1,500 ($30,000 total). This is a flat rate that doesn't require tracking expenses or calculating the business use percentage.\\n2. **Elimination of the Business Use Percentage Method**: The TCJA eliminated the business use percentage method, which allowed self-employed individuals and sole proprietors to calculate their home office deduction based on the square footage of the space used for business. This method was phased out over a three-year period from 2018 to 2025.\\n3. **No Deduction Limitations**: The TCJA eliminated the $25,000 limitation on the home office deduction that applied to self-employed individuals and sole proprietors who were not in the active conduct of a trade or business. This means that more self-employed individuals can now deduct their home office expenses without being subject to this limit.\\n4. **No Carryover**: The TCJA eliminated the ability to carry over unused home office deductions from 2018 to 2025, which was previously allowed under the previous law.\\n\\nOverall, the simplified option provides a more straightforward and easier-to-use method for self-employed individuals and sole proprietors to deduct their home office expenses. However, it's essential to note that this new method is only available to those who are not in the active conduct of a trade or business, such as freelancers, consultants, or independent contractors.\\n\\nIt's always recommended to consult with a tax professional to determine which option is best for your specific situation and to ensure you're taking advantage of all eligible deductions.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I can tell you that yes, self-employed individuals can deduct business meals on their tax return, but there are some rules and limitations to be aware of.\\n\\nThe IRS allows self-employed individuals to deduct the cost of business meals as a miscellaneous itemized deduction on Schedule C (Form 1040), which is used for sole proprietorships and single-member limited liability companies (LLCs).\\n\\nTo qualify for this deduction, the meal must meet certain requirements:\\n\\n1. The meal must be for business or business purposes.\\n2. The meal must be with a client, customer, or prospective client.\\n3. The meal cannot be primarily for entertainment or recreation.\\n\\nHere are some examples of eligible meals:\\n\\n* Business lunches with clients or customers\\n* Breakfast meetings with potential clients\\n* Traveling to and from a meeting or conference\\n* Meals at conferences or trade shows\\n\\nHowever, the following types of meals are not eligible for deduction:\\n\\n* Social gatherings, such as birthday parties or holiday celebrations\\n* Meals that are primarily for entertainment or recreation\\n* Meals that are not related to business activities\\n\\nTo deduct business meals, you'll need to keep accurate records, including:\\n\\n1. Receipts and invoices from the restaurant or catering service\\n2. A log of the date, time, location, and purpose of each meal\\n3. The names and titles of the individuals present (if applicable)\\n\\nThe IRS allows a standard deduction of $5 per meal for meals with clients or customers, but this can be adjusted based on the cost of the meal.\\n\\nIt's also worth noting that the Tax Cuts and Jobs Act (TCJA) suspended the 50% limit on business meal deductions from 2018 to 2025. However, after 2025, the 50% limit will return.\\n\\nAs a tax preparer, I always recommend keeping accurate records and consulting with a tax professional to ensure you're taking advantage of all eligible deductions and following the correct procedures for claiming business meals on your tax return.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I'd be happy to explain how self-employed individuals can report 1099 income from a partnership.\\n\\nWhen you're a partner in a partnership, you receive a Form 1099-K from the partnership at the end of each year. This form shows the total amount of money you received from the partnership during the tax year. However, as a self-employed individual, you need to report this income on your personal tax return.\\n\\nHere's how to report 1099 income from a partnership:\\n\\n1. **Report the income on Schedule C (Form 1040)**: You'll report the 1099-K income on Schedule C (Form 1040), which is the form used for self-employment income and expenses.\\n2. **Complete Form 1065**: As a partner, you're also required to file a partnership return with the IRS using Form 1065. This form reports the partnership's income, deductions, and credits. You'll need to attach this form to your personal tax return (Form 1040).\\n3. **Report business use of home**: If you used a dedicated space in your home for business purposes, you may be able to deduct a portion of your rent or mortgage interest as a business expense on Schedule C.\\n4. **Business expenses**: You can also report business-related expenses on Schedule C, such as travel expenses, equipment purchases, and supplies.\\n5. **Self-employment tax**: As a self-employed individual, you're responsible for paying self-employment tax (SE tax) on your net earnings from self-employment. This is reported on Schedule SE (Form 1040).\\n6. **Estimated tax payments**: If you expect to owe more than $1,000 in taxes for the year, you may need to make estimated tax payments throughout the year using Form 1040-ES.\\n\\nSome important notes:\\n\\n* You'll need to keep accurate records of your partnership income and expenses, as well as any business-related documents, such as invoices, receipts, and bank statements.\\n* If you're a partner in a limited liability company (LLC), you may be able to report the income on Schedule C or Form 1040, depending on how the LLC is structured.\\n* It's always a good idea to consult with a tax professional or accountant to ensure you're meeting all the necessary reporting requirements and taking advantage of available deductions.\\n\\nI hope this helps! Let me know if you have any other questions.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"The penalty for not reporting 1099 income on a tax return can vary depending on several factors, including the amount of unreported income, the taxpayer's intent, and whether they have made an honest effort to comply with their tax obligations.\\n\\n Generally, the IRS imposes penalties for failing to report 1099 income on Form 1040. The penalty is calculated as follows:\\n\\n1. The first $500 of unreported 1099 income is not subject to penalty.\\n2. For amounts between $500 and $5,000, the penalty is 20% of the amount of unreported income.\\n3. For amounts over $5,000, the penalty is 40% of the amount of unreported income.\\n\\nIn addition to the penalty, you may also be subject to interest on the unreported income from the date it was due.\\n\\nIt's worth noting that there are some exceptions and mitigating factors that can affect the penalty, such as:\\n\\n* If you have an honest effort to comply with your tax obligations, but made a reasonable mistake or error.\\n* If you have filed Form 2210, which is used to request abatement of penalties for failure to report income.\\n* If you are a first-time filer and meet certain requirements.\\n\\nIt's always best to consult with a tax professional or the IRS directly to determine the specific penalty and any potential relief options.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'As a tax preparer, I\\'d be happy to help clarify this for you.\\n\\nSelf-employed individuals can indeed deduct self-employment tax on their tax return, but there are some important nuances to understand.\\n\\nThe Self-Employment Tax (SE) is a type of payroll tax that covers Social Security and Medicare taxes. As a self-employed individual, you\\'re responsible for paying both the employer and employee portions of these taxes, which is why it\\'s called \"self-employment tax.\"\\n\\nTo deduct self-employment tax on your tax return, you\\'ll need to calculate the net earnings from self-employment and then subtract any qualified retirement plan contributions. Here are the steps:\\n\\n1. Calculate your net earnings from self-employment: This includes income from your business or freelance work, minus any business expenses.\\n2. Determine your self-employment tax liability: You can use Form 1040 to calculate this amount using Schedule SE (Self-Employment Tax).\\n3. Subtract qualified retirement plan contributions: If you made contributions to a SEP-IRA, solo 401(k), or other qualified plans, you can subtract these contributions from your net earnings from self-employment.\\n4. Calculate the self-employment tax deduction: This is the amount of self-employment tax you paid during the year.\\n\\nThe standard rate for self-employment tax is 15.3% of your net earnings from self-employment, which includes:\\n\\n* 12.4% for Social Security (old-age, survivors, and disability insurance)\\n* 2.9% for Medicare (hospital insurance)\\n\\nHowever, you may be able to deduct half of this amount as a business expense on Schedule C (Form 1040), which can help reduce your taxable income.\\n\\nIt\\'s essential to note that the self-employment tax deduction is subject to certain limits and phase-outs. For example:\\n\\n* The net earnings from self-employment limit: If your net earnings from self-employment exceed $400, you\\'re required to make estimated tax payments throughout the year.\\n* Phase-out of self-employment tax deduction: If your adjusted gross income exceeds a certain threshold (currently $160,200 for single filers and $320,400 for joint filers), the self-employment tax deduction may be phased out.\\n\\nTo ensure accurate calculations and compliance with IRS regulations, it\\'s always best to consult with a tax professional or use tax preparation software that can guide you through the process.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I've seen my fair share of missing or incorrect 1099 forms from self-employed individuals. Here's how they typically handle these situations:\\n\\n**Missing 1099 Form:**\\n\\nIf a self-employed individual receives a missing 1099 form, they should follow these steps:\\n\\n1. **Contact the payer**: Reach out to the payer (e.g., client, contractor, or freelancer) and ask for a replacement copy of the 1099 form.\\n2. **Request an amended 1099**: If the payer is unable to provide a replacement copy, request that they file an amended 1099 with the IRS by the original filing deadline (usually April 15th).\\n3. **File Form 4852**: The self-employed individual may need to complete Form 4852, Substitute for Form W-2, Wage and Tax Statement, if they don't receive a 1099 form from their payer.\\n4. **Report income on Schedule C**: On their tax return (Form 1040), the self-employed individual will report the missing income on Schedule C (Form 1040), which is the business income and expenses schedule.\\n\\n**Incorrect 1099 Form:**\\n\\nIf a self-employed individual receives an incorrect 1099 form, they should:\\n\\n1. **Review the form carefully**: Check for any errors or discrepancies in the information reported.\\n2. **Contact the payer**: Reach out to the payer and request that they correct the error(s) on the 1099 form.\\n3. **Request a corrected 1099**: If the payer is unable to correct the error, ask them to file an amended 1099 with the IRS by the original filing deadline (usually April 15th).\\n4. **Report income correctly on Schedule C**: On their tax return (Form 1040), the self-employed individual will report the corrected income on Schedule C.\\n\\n**Additional Tips:**\\n\\n* Self-employed individuals should keep a record of all correspondence with their payer, including dates and details of conversations or emails.\\n* If the error is significant (e.g., incorrect amount or type of income), it may be beneficial to seek professional help from a tax preparer or accountant to ensure accurate reporting on their tax return.\\n* In some cases, self-employed individuals may need to file Form 1040X (Amended U.S. Individual Income Tax Return) if they discover errors or discrepancies after filing their original tax return.\\n\\nBy following these steps, self-employed individuals can minimize the impact of a missing or incorrect 1099 form and ensure accurate reporting on their tax return.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I can tell you that yes, self-employed individuals can amend their tax return if they receive a corrected 1099 form.\\n\\nIf a self-employed individual receives a corrected 1099 form from an employer or client, it's essential to file an amended tax return (Form 1040X) to reflect the corrected income. Here are some scenarios where amending is necessary:\\n\\n1. **Corrected income**: If the corrected 1099 form shows that you received more or less income than initially reported on your original tax return, you'll need to amend your return to reflect the correct amount.\\n2. **Incorrect income reporting**: If the corrected 1099 form indicates an error in the amount of income reported, such as a miscalculation or incorrect payment, you should file an amended return to correct this discrepancy.\\n3. **Missing income**: If the corrected 1099 form reveals that you missed reporting any income on your original tax return, you'll need to amend your return to include this additional income.\\n\\nTo amend your tax return, follow these steps:\\n\\n1. Gather all relevant documents, including the corrected 1099 form and any other supporting documentation.\\n2. Complete Form 1040X, which is the amended U.S. Individual Income Tax Return.\\n3. Attach a copy of the corrected 1099 form to the amended return.\\n4. File the amended return with the IRS by the original filing deadline (usually April 15th for individual tax returns) or within three years from the original filing date, whichever is later.\\n\\nKeep in mind that you'll need to provide documentation to support your amended return, such as:\\n\\n* The corrected 1099 form\\n* Any other relevant financial records, like bank statements or cancelled checks\\n* A written explanation of the error and how it was corrected\\n\\nIt's essential to note that amending a tax return can be complex, so if you're unsure about the process or need help with the amended return, consider consulting a tax professional, such as myself!\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I can tell you that the deadline for receiving a 1099 form varies depending on the type of income and the payer.\\n\\nFor most types of income, such as freelance work, independent contracting, or self-employment income, the deadline for receiving a 1099-MISC (Miscellaneous Income) form is January 31st of each year. This means that by January 31st, you should receive a copy of your 1099-MISC from any payer who paid you $600 or more in a calendar year.\\n\\nHowever, there are some exceptions to this deadline:\\n\\n* For payments made through a third-party payment service, such as PayPal or Venmo, the deadline is February 1st.\\n* For payments made by a corporation, the deadline is January 31st for corporations that file Form 1099-K (Payment Card and Third-Party Network Transactions) with the IRS.\\n* For payments made to non-resident aliens, the deadline is March 15th.\\n\\nIt's also worth noting that some states may have different deadlines for receiving 1099 forms. As a tax preparer, I would recommend checking with your state's tax authority to confirm their specific deadline.\\n\\nAs a general rule of thumb, it's always best to receive your 1099 form by January 31st to ensure you can accurately report your income on your tax return and avoid any potential penalties or interest.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I'd be happy to explain how self-employed individuals can report 1099 income on their tax return.\\n\\nSelf-employment income is reported on Schedule C (Form 1040), which is the form used for sole proprietorships and single-member limited liability companies (LLCs). Here's a step-by-step guide:\\n\\n1. **Gather all 1099 forms**: Collect all 1099-MISC forms from clients, customers, or vendors who paid you $600 or more in a calendar year. These forms will show the amount of money you earned from each client.\\n2. **Calculate business income**: Add up the total amount of 1099 income received throughout the year. This includes income from freelance work, consulting, selling products or services, and any other self-employment activities.\\n3. **Complete Schedule C (Form 1040)**: On Schedule C, report your business income on Line 1. You'll also need to calculate your business expenses on this form, which will be discussed later.\\n4. **Calculate net profit or loss**: Subtract your business expenses from your business income on Line 2 of Schedule C. This will give you your net profit or loss for the year.\\n5. **Complete Form 1040**: Report your net profit or loss from Schedule C on Line 31 of Form 1040. If you have a net profit, this amount will be reported as ordinary income on your tax return.\\n\\n**Business Expenses:**\\n\\nAs a self-employed individual, you can deduct business expenses on Schedule C to reduce your taxable income. Common business expenses include:\\n\\n* Home office expenses (e.g., rent, utilities, equipment)\\n* Travel expenses\\n* Business use of your car or other vehicles\\n* Meals and entertainment expenses (subject to certain limits)\\n* Business-related travel expenses\\n* Professional fees (e.g., lawyer, accountant, insurance)\\n\\n**Self-Employment Tax:**\\n\\nAs a self-employed individual, you're responsible for paying self-employment tax on your net earnings from self-employment. This tax is used to fund Social Security and Medicare. The self-employment tax rate is 15.3% of your net earnings from self-employment, which includes:\\n\\n* 12.4% for Social Security (old-age, survivors, and disability insurance)\\n* 2.9% for Medicare (hospital insurance)\\n\\nYou'll report this tax on Schedule SE (Form 1040), which is attached to Form 1040.\\n\\n**Estimated Tax Payments:**\\n\\nAs a self-employed individual, you're required to make estimated tax payments throughout the year if you expect to owe $1,000 or more in taxes. You can use Form 1040-ES to make these payments.\\n\\nThat's a general overview of how self-employed individuals report 1099 income on their tax return. If you have any specific questions or concerns, it's always best to consult with a tax professional like myself for personalized guidance!\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I'd be happy to help clarify this for you.\\n\\nYes, self-employed individuals can deduct business expenses on their 1099 income. In fact, one of the benefits of being self-employed is that you can deduct business expenses related to your trade or business as an expense on your tax return.\\n\\nThe IRS allows self-employed individuals to deduct business expenses on Schedule C (Form 1040), which is used to report business income and expenses. This includes:\\n\\n1. Business use of your home: If you use a dedicated space in your home for business, you can deduct the business use percentage of your rent or mortgage interest, utilities, and other expenses.\\n2. Business travel expenses: You can deduct expenses related to business travel, such as transportation, meals, lodging, and entertainment.\\n3. Equipment and supplies: You can deduct the cost of equipment, software, and supplies used for your business.\\n4. Advertising and marketing expenses: You can deduct expenses related to promoting your business, such as website development, advertising, and promotional materials.\\n5. Business use of your car: If you use your car for business purposes, you can deduct the business use percentage of your car expenses, including gas, maintenance, and insurance.\\n6. Professional fees: You can deduct fees paid to professionals, such as lawyers, accountants, and consultants, who provide services related to your business.\\n7. Business education and training: You can deduct expenses related to courses or workshops that improve your skills or knowledge in your trade or business.\\n\\nTo qualify for these deductions, you must have records to support the expense, including receipts, invoices, and bank statements. It's also important to keep accurate records of your business income and expenses throughout the year, as this will help you complete your tax return accurately and avoid any potential audits.\\n\\nSome important notes:\\n\\n* You can only deduct expenses that are directly related to your business.\\n* You cannot deduct personal expenses, such as charitable donations or medical expenses, unless they are also business-related.\\n* The IRS has specific rules for deducting home office expenses, including the 5% rule, which allows you to deduct a portion of your rent or mortgage interest based on the square footage used for business.\\n\\nIt's always a good idea to consult with a tax professional, like myself, to ensure you're taking advantage of all the deductions available to you and following the IRS guidelines.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I'd be happy to explain how self-employed individuals calculate their self-employment tax.\\n\\nSelf-employment tax is used to fund Social Security and Medicare, just like payroll taxes for employees. However, self-employed individuals are responsible for paying both the employee and employer portions of these taxes, which can add up quickly.\\n\\nHere's a step-by-step guide on how self-employed individuals calculate their self-employment tax:\\n\\n1. **Calculate your net earnings from self-employment**: Start by calculating your total income from all sources related to your business or freelance work. This includes:\\n\\t* Business income (e.g., cash, checks, credit card payments)\\n\\t* Freelance income\\n\\t* Rent or royalty income\\n\\t* Any other income related to your business\\n2. **Deduct business expenses**: Subtract business expenses from your total income to determine your net earnings from self-employment. This will help reduce your taxable income.\\n3. **Calculate the self-employment tax rate**: The self-employment tax rate is 15.3% of your net earnings from self-employment, which includes:\\n\\t* 12.4% for Social Security (old-age, survivors, and disability insurance)\\n\\t* 2.9% for Medicare (hospital insurance)\\n4. **Calculate the self-employment tax**: Multiply your net earnings from self-employment by the self-employment tax rate (15.3%). This will give you the total amount of self-employment tax due.\\n5. **Add half of your Social Security tax to your income**: Since self-employed individuals pay both the employee and employer portions of payroll taxes, you'll need to add half of your Social Security tax to your income. This is calculated as:\\n\\t* 6.2% of your net earnings from self-employment (half of the 12.4% rate)\\n6. **Calculate your total self-employment tax**: Add the self-employment tax and the additional Social Security tax to get your total self-employment tax liability.\\n\\nExample:\\n\\nLet's say you have a net income from self-employment of $50,000 and business expenses of $20,000, leaving you with $30,000 in taxable income. Your self-employment tax rate is 15.3%.\\n\\n1. Calculate the self-employment tax: $30,000 x 15.3% = $4,590\\n2. Add half of your Social Security tax: $30,000 x 6.2% = $1,860\\n3. Calculate your total self-employment tax: $4,590 + $1,860 = $6,450\\n\\nIn this example, the self-employed individual would need to pay a total of $6,450 in self-employment tax.\\n\\nKeep in mind that you can deduct half of your self-employment tax as a business expense on Schedule C (Form 1040), which can help reduce your taxable income. It's always a good idea to consult with a tax professional or accountant to ensure accurate calculations and to take advantage of any available deductions.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'As a tax preparer, I can tell you that self-employment tax applies to income from various sources, including:\\n\\n1. **Business income**: Income earned from running your own business, such as freelancing, consulting, or starting a side hustle.\\n2. **Self-employment income**: Income earned from working for yourself, such as:\\n\\t* Independent contractor work\\n\\t* Freelance writing, designing, or other creative services\\n\\t* Renting out a room on Airbnb\\n\\t* Selling products online through an e-commerce platform\\n3. **Unemployment benefits**: Some states tax unemployment benefits as self-employment income.\\n4. **Alimony paid to ex-spouses**: Alimony payments made by one spouse to the other are considered self-employment income and subject to self-employment tax.\\n5. **Royalties**: Income from intellectual property, such as book royalties or music royalties, is also subject to self-employment tax.\\n\\nSelf-employment tax applies because you\\'re considered self-employed and must report this income on your tax return. As a self-employed individual, you\\'re responsible for paying both the employee and employer portions of payroll taxes, which includes:\\n\\n* 12.4% for Social Security (old-age, survivors, and disability insurance)\\n* 2.9% for Medicare (hospital insurance)\\n\\nThis total is often referred to as your \"self-employment tax rate.\" You\\'ll need to pay this amount on a quarterly basis using Form 1040-ES.\\n\\nKeep in mind that some states may have different rules or exemptions from self-employment tax, so it\\'s always best to consult with a tax professional or check with your state\\'s tax authority for specific guidance.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I'd be happy to explain how self-employed individuals can report their business income and expenses.\\n\\nSelf-employed individuals who have a business or freelance work must report their income and expenses on their personal tax return. Here's a step-by-step guide:\\n\\n**Reporting Business Income:**\\n\\n1. **Business Income:** Self-employed individuals must report all business income, including:\\n\\t* Cash payments from clients\\n\\t* Accounts receivable (amounts owed to them by customers)\\n\\t* Interest income from business-related investments\\n\\t* Royalties or other passive income\\n2. **Self-Employment Tax:** If you're self-employed, you'll need to pay self-employment tax on your net earnings from self-employment. This includes:\\n\\t* Net earnings from self-employment (business income minus business expenses)\\n\\t* Half of your net earnings from self-employment (for Social Security and Medicare taxes)\\n\\n**Reporting Business Expenses:**\\n\\n1. **Business Expense Records:** Keep accurate records of all business-related expenses, including:\\n\\t* Receipts\\n\\t* Invoices\\n\\t* Bank statements\\n\\t* Credit card statements\\n2. **Business Expense Categories:** Categorize your expenses into the following categories:\\n\\t* Operating expenses (e.g., rent, utilities, supplies)\\n\\t* Business use of your home (if you work from home)\\n\\t* Travel expenses\\n\\t* Advertising and marketing expenses\\n\\t* Professional fees (e.g., lawyer, accountant)\\n3. **Business Expense Deductions:** Claim deductions for business expenses that are ordinary and necessary for the operation of your business.\\n\\n**Common Business Expense Deductions:**\\n\\n1. Home office deduction (if you work from home)\\n2. Business use of your car\\n3. Travel expenses (mileage, meals, lodging)\\n4. Professional fees (e.g., lawyer, accountant)\\n5. Advertising and marketing expenses\\n\\n**Reporting Business Expenses on the Tax Return:**\\n\\n1. **Schedule C (Form 1040):** Complete Schedule C to report business income and expenses.\\n2. **Business Use of Your Home:** If you work from home, complete Form 8829 to calculate your home office deduction.\\n3. **Business Expense Deductions:** Report business expense deductions on Schedule A (Itemized Deductions) or on a separate form (e.g., Form 2106 for car expenses).\\n\\n**Important Notes:**\\n\\n1. Keep accurate records of all business income and expenses throughout the year, as these will be used to complete your tax return.\\n2. Consult with a tax professional if you're unsure about any aspect of reporting business income and expenses.\\n3. Self-employed individuals may need to file additional forms, such as Form 1040-ES (Estimated Tax for Individuals) or Schedule SE (Self-Employment Tax).\\n\\nRemember, accurate and timely reporting of business income and expenses is crucial to avoid penalties and interest on underreported income or unclaimed deductions.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"The self-employment tax rate for net earnings from self-employment is 15.3% of your net earnings from self-employment, which includes income from freelance work, consulting, or running a business.\\n\\nThis rate consists of two parts:\\n\\n1. The employee portion: 12.4% (6.2% for Social Security and 6.2% for Medicare)\\n2. The employer portion: 2.9% (1.45% for Social Security and 1.45% for Medicare)\\n\\nSince you are self-employed, you are both the employee and the employer, so you pay both parts of the tax.\\n\\nTo calculate your self-employment tax, you'll need to add the employee portion and the employer portion together:\\n\\n12.4% (employee portion) + 2.9% (employer portion) = 15.3%\\n\\nSo, for every dollar you earn from self-employment, you pay 15.3% in self-employment tax.\\n\\nKeep in mind that this rate applies to your net earnings from self-employment, which is your total income minus any business expenses and deductions.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I can tell you that yes, self-employed individuals can deduct business use of their home as a business expense, but there are some requirements and limitations to be aware of.\\n\\nTo qualify for the home office deduction, the space used for business must meet certain criteria:\\n\\n1. **Business use percentage**: The space must be used regularly and exclusively for business purposes. This means that you can't simply convert a spare room into a home office just to claim a deduction.\\n2. **Business use of at least 5%**: The space must be used by the self-employed individual for business purposes for at least 5% of the total square footage of the home.\\n3. **Home office is used as a regular and necessary business expense**: The home office must be used regularly and be necessary for the conduct of your trade or business.\\n\\nTo calculate the deduction, you'll need to determine the business use percentage of your home. You can do this by:\\n\\n1. Measuring the square footage of the space used for business.\\n2. Calculating the total square footage of your home.\\n3. Dividing the business use square footage by the total square footage.\\n\\nFor example, if your home office is 100 square feet and your total home size is 1,500 square feet, you can calculate the business use percentage as follows:\\n\\nBusiness use percentage = (100 sq ft / 1,500 sq ft) x 100% = 6.67%\\n\\nOnce you have the business use percentage, you can deduct a portion of your rent or mortgage interest and utilities as a business expense.\\n\\n**Types of expenses that can be deducted:**\\n\\n* Rent or mortgage interest\\n* Property taxes (if not included in the mortgage)\\n* Utilities (electricity, gas, water, internet, etc.)\\n* Home maintenance and repairs\\n\\n**Record-keeping is key:**\\n\\nTo support your home office deduction, keep accurate records of:\\n\\n1. Business use percentage calculations\\n2. Square footage measurements\\n3. Rent or mortgage statements\\n4. Utility bills\\n5. Maintenance and repair receipts\\n\\nIt's essential to maintain these records for at least 3 years in case of an audit.\\n\\n**Important notes:**\\n\\n* The home office deduction is subject to the $25,000 limit per year (pre-2018) or $10,000 limit per year (post-2017).\\n* If you're married and file jointly, you can deduct half of the business use percentage.\\n* You may need to complete Form 8829 (Expenses for Business Use of Your Home) to claim the deduction.\\n\\nAs a tax preparer, I recommend consulting with me or a qualified tax professional to ensure you meet all the requirements and follow the correct procedures for claiming the home office deduction.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I'd be happy to explain how self-employed individuals calculate their net earnings from self-employment for self-employment tax purposes.\\n\\nSelf-employment income is subject to both the employee and employer portions of payroll taxes, which includes Social Security and Medicare taxes. As a self-employed individual, you are responsible for paying both the employee and employer portions of these taxes, which can be a bit more complicated than just taking the standard deduction.\\n\\nTo calculate net earnings from self-employment, follow these steps:\\n\\n1. **Calculate your total gross income**: Start by calculating your total gross income from all sources, including:\\n\\t* Business income (e.g., freelance work, consulting, or running a business)\\n\\t* Rent or royalty income\\n\\t* Interest, dividends, and capital gains\\n2. **Deduct business expenses**: Subtract business expenses related to your self-employment income, such as:\\n\\t* Business use of your home (home office deduction)\\n\\t* Travel expenses\\n\\t* Equipment, supplies, and materials\\n\\t* Rent or mortgage interest\\n\\t* Utilities\\n\\t* Insurance premiums\\n\\t* Advertising and marketing expenses\\n\\t* Professional fees (e.g., lawyer, accountant, or consultant fees)\\n3. **Calculate net earnings from self-employment**: Subtract your business expenses from your total gross income to get your net earnings from self-employment.\\n4. **Calculate the self-employment tax**: Calculate the self-employment tax by using Schedule SE (Form 1040) and the following formula:\\n\\nSelf-Employment Tax = Net Earnings from Self-Employment x 15.3% (12.4% for Social Security + 2.9% for Medicare)\\n\\nThis rate is applied to your net earnings from self-employment, but you can deduct half of this amount as a credit on Schedule SE.\\n\\n5. **Calculate the self-employment tax deduction**: You can deduct half of your self-employment tax as an above-the-line deduction on Form 1040, which reduces your taxable income.\\n6. **Report net earnings from self-employment on Schedule C (Form 1040)**: Report your net earnings from self-employment on Schedule C, which is the business income and expense schedule.\\n\\nExample:\\n\\nLet's say John is a freelancer who earns $50,000 in gross income from his freelance work. He has business expenses of $15,000, including home office expenses, travel expenses, and equipment purchases. His net earnings from self-employment would be:\\n\\nNet Earnings from Self-Employment = Gross Income - Business Expenses\\n= $50,000 - $15,000\\n= $35,000\\n\\nTo calculate the self-employment tax:\\n\\nSelf-Employment Tax = Net Earnings from Self-Employment x 15.3%\\n= $35,000 x 0.153\\n= $5,405\\n\\nJohn would report his net earnings from self-employment on Schedule C and pay self-employment tax of $5,405. He can deduct half of this amount as a credit on Schedule SE.\\n\\nKeep in mind that this is just an example, and your specific situation may be more complex. It's always best to consult with a tax professional or accountant to ensure you're accurately calculating your net earnings from self-employment and taking advantage of all the deductions available to you.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'As a tax preparer, I can tell you that yes, self-employed individuals can deduct their health insurance premiums as a business expense on their tax return.\\n\\nThe IRS allows self-employed individuals to deduct the cost of health insurance premiums for themselves and their family members as a business expense if they are required to pay these premiums because of their self-employment income. This is known as the \"self-employment health plan deduction.\"\\n\\nTo qualify for this deduction, you must meet certain requirements:\\n\\n1. You must be self-employed and have net earnings from self-employment of $100 or more.\\n2. You must purchase a qualified health insurance policy that covers you and your family members.\\n3. The policy must be purchased through the Health Insurance Marketplace (also known as an \"individual shared responsibility payment\") or through a group plan offered by an employer.\\n\\nThe deduction is calculated based on the amount of premiums paid for yourself, your spouse, and any dependents who are covered under the policy. You can deduct the full premium amount, but you may need to adjust it if you have other sources of income that reduce your self-employment net earnings from self-employment.\\n\\nHere\\'s an example:\\n\\nLet\\'s say John is a sole proprietor with $50,000 in net earnings from self-employment and he pays $1,500 per month for health insurance premiums. He can deduct the full $1,500 as a business expense on his tax return.\\n\\nHowever, if John has other sources of income that reduce his net earnings from self-employment to $40,000, he can only deduct the amount of the premium that reduces his net earnings by $10,000 ($50,000 - $40,000 = $10,000). In this case, John would deduct $1,500 (the full premium) minus $10,000 (the reduced net earnings), which is $900.\\n\\nIt\\'s always a good idea to keep accurate records of your health insurance premiums and other business expenses to ensure you can accurately calculate the deduction on your tax return.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'As a tax preparer, I\\'d be happy to explain the differences between a sole proprietorship and a single-member Limited Liability Company (LLC) for tax purposes.\\n\\n**Sole Proprietorship:**\\n\\nA sole proprietorship is a business owned and operated by one individual. The owner reports their business income on their personal tax return (Form 1040). As a sole proprietor, the business income is reported as \"net earnings from self-employment\" on Schedule C (Form 1040), which is attached to the personal tax return.\\n\\nThe key characteristics of a sole proprietorship for tax purposes are:\\n\\n* The owner reports all business income and expenses on their personal tax return.\\n* Business losses can be used to offset other income, but not carried over to future years.\\n* Self-employment taxes (Social Security and Medicare taxes) are calculated based on net earnings from self-employment.\\n\\n**Single-Member LLC:**\\n\\nA single-member Limited Liability Company (LLC), also known as a \"disregarded entity,\" is a business owned by one individual. For tax purposes, the single-member LLC is treated as a sole proprietorship. The owner reports all business income and expenses on their personal tax return (Form 1040).\\n\\nHowever, there are some key differences:\\n\\n* A single-member LLC can elect to be taxed as a pass-through entity, meaning that the business income is passed through to the individual\\'s personal tax return, just like a sole proprietorship.\\n* Business losses can be carried over to future years and used to offset other income.\\n* Self-employment taxes are calculated based on net earnings from self-employment.\\n\\n**Key differences:**\\n\\nThe main difference between a single-member LLC and a sole proprietorship is the level of liability protection. As a sole proprietor, the owner\\'s personal assets are at risk in case of business debts or lawsuits. In contrast, a single-member LLC provides some level of liability protection, as the business is separate from the individual.\\n\\nHowever, for tax purposes, a single-member LLC and a sole proprietorship are treated similarly. The business income is reported on the same tax return, and self-employment taxes are calculated in the same way.\\n\\n**When to choose an LLC:**\\n\\nWhile a single-member LLC may not provide significant tax benefits over a sole proprietorship, there are situations where it might be beneficial:\\n\\n* Liability protection: If you want to protect your personal assets from business debts or lawsuits.\\n* Flexibility: An LLC can elect to be taxed as a pass-through entity, which allows for more control over taxes and flexibility in the future.\\n\\nIn summary, while both sole proprietorships and single-member LLCs are treated similarly for tax purposes, an LLC provides some level of liability protection that may be attractive to business owners. However, the tax benefits are relatively minor, and the decision ultimately depends on your individual circumstances and goals.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I'd be happy to explain how self-employed individuals can report self-employment income from a partnership.\\n\\nWhen it comes to reporting self-employment income from a partnership, there are some specific rules and requirements that need to be followed. Here's a step-by-step guide:\\n\\n1. **Form 1065**: The partnership must file Form 1065, U.S. Return of Partnership Income (Information), with the IRS by March 15th of each year. This form reports the partnership's income, deductions, and credits.\\n2. **K-1 Forms**: Each partner receives a Schedule K-1 (Form 1065) from the partnership, which shows their share of the partnership's income, deductions, and credits for the tax year. The K-1 forms are used by each partner to report their individual tax return.\\n3. **Self-Employment Income**: Self-employment income from a partnership is reported on Schedule C (Form 1040), which is the form used to report business income and expenses. The self-employment income includes:\\n\\t* Business income from the partnership\\n\\t* Any other self-employment income, such as freelance work or consulting fees\\n4. **Business Expenses**: Self-employed individuals can deduct business expenses related to their partnership activities on Schedule C (Form 1040). These expenses may include:\\n\\t* Business use of a home or car\\n\\t* Travel expenses\\n\\t* Equipment and supplies\\n\\t* Rent or mortgage interest\\n\\t* Utilities\\n5. **Self-Employment Tax**: Self-employed individuals must pay self-employment tax, which includes both the employee and employer portions of payroll taxes (Social Security and Medicare taxes). This is reported on Schedule SE (Form 1040).\\n6. **Estimated Taxes**: Self-employed individuals are required to make estimated tax payments throughout the year if they expect to owe $1,000 or more in taxes for the year. These payments are made using Form 1040-ES.\\n7. **Quarterly Estimated Tax Payments**: The due dates for quarterly estimated tax payments are:\\n\\t* April 15th for Q1 (January 1 - March 31)\\n\\t* June 15th for Q2 (April 1 - May 31)\\n\\t* September 15th for Q3 (June 1 - August 31)\\n\\t* January 15th of the following year for Q4 (September 1 - December 31)\\n\\nIt's essential to note that self-employed individuals may need to file additional forms, such as Form 8829 (Expenses for Business Use of Your Home) if they use a home office for business purposes.\\n\\nAs a tax preparer, I would work with the partnership and each partner to ensure accurate reporting of self-employment income from the partnership on their individual tax returns.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"As a tax preparer, I can tell you that yes, self-employed individuals can deduct their retirement plan contributions as a business expense on their tax return.\\n\\nSelf-employment income is subject to self-employment taxes, which include both the employee and employer portions of payroll taxes. However, self-employed individuals can deduct half of their net earnings from self-employment, including retirement plan contributions, as a business expense.\\n\\nThere are several types of retirement plans that qualify for deduction as a business expense:\\n\\n1. SEP-IRA (Simplified Employee Pension Individual Retirement Account): Contributions to a SEP-IRA are deductible as a business expense.\\n2. Solo 401(k) or Individual 401(k): Contributions to a solo 401(k) or individual 401(k) plan are deductible as a business expense.\\n3. Traditional IRA: Contributions to a traditional IRA may be deductible as a business expense, but only if the self-employed individual is not covered by another retirement plan at work.\\n4. Solo 403(b) or Thrift Savings Plan: Contributions to a solo 403(b) or thrift savings plan are deductible as a business expense.\\n\\nTo qualify for this deduction, you must meet certain requirements, such as:\\n\\n* Being self-employed and having net earnings from self-employment\\n* Making contributions to the retirement plan within the plan's contribution limits\\n* Having a valid business purpose for making the contributions (e.g., to save for retirement)\\n\\nIt's essential to keep accurate records of your retirement plan contributions, including receipts, bank statements, and any other documentation that supports your deductions. You should also consult with a tax professional or financial advisor to ensure you're meeting all the requirements and taking advantage of the deductions available to you.\\n\\nKeep in mind that deducting retirement plan contributions as a business expense can impact your self-employment taxes, so it's crucial to understand how this affects your overall tax situation.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'As a tax preparer, I\\'d be happy to explain how self-employed individuals can calculate their self-employment tax on a net loss from self-employment.\\n\\nSelf-employment tax is used to fund Social Security and Medicare. As a self-employed individual, you are responsible for paying both the employee and employer portions of these taxes, which is why it\\'s called \"self-employment tax.\" The self-employment tax rate is 15.3% of your net earnings from self-employment, which includes income from freelance work, consulting, or running a business.\\n\\nTo calculate self-employment tax on a net loss from self-employment, you\\'ll need to follow these steps:\\n\\n1. Calculate your net profit or loss from self-employment: Start by calculating your total gross income from self-employment and subtract any business expenses, deductions, and credits that reduce your taxable income.\\n2. Determine your net earnings from self-employment: This is the amount of money you have left after deducting all eligible business expenses and other deductions from your gross income.\\n3. Calculate the self-employment tax: Multiply your net earnings from self-employment by 15.3% (12.4% for Social Security and 2.9% for Medicare).\\n4. Consider any adjustments to the self-employment tax rate: If you\\'re eligible for certain deductions or credits that reduce your taxable income, such as the home office deduction or business use of your car, these may affect your self-employment tax rate.\\n\\nHere\\'s an example:\\n\\nLet\\'s say John is a freelancer who earns $50,000 in gross income from his freelance work. He has business expenses of $20,000 and other deductions that reduce his taxable income to $30,000. His net earnings from self-employment are $30,000.\\n\\nTo calculate the self-employment tax:\\n\\n1. Calculate net earnings: $30,000 (net earnings) - $0 (no business use of home or car expenses in this example)\\n2. Calculate self-employment tax: 15.3% of $30,000 = $4,590\\n\\nIn this example, John\\'s self-employment tax would be $4,590.\\n\\nHowever, if John has a net loss from self-employment, he can deduct the loss on his tax return and reduce his taxable income. If John has a net loss of $10,000, his new net earnings from self-employment would be:\\n\\n$30,000 (previous net earnings) - $10,000 (net loss) = $20,000\\n\\nTo calculate the self-employment tax on this reduced amount:\\n\\n1. Calculate net earnings: $20,000\\n2. Calculate self-employment tax: 15.3% of $20,000 = $3,060\\n\\nIn this example, John\\'s self-employment tax would be $3,060.\\n\\nKeep in mind that these are simplified examples and actual calculations may vary depending on individual circumstances. It\\'s always a good idea to consult with a tax professional or accountant to ensure accurate calculations and take advantage of all eligible deductions and credits.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'As a tax preparer, I can provide guidance on this topic.\\n\\nSelf-employed individuals can indeed deduct business expenses related to the production of income that is exempt from self-employment tax. However, there are some important nuances to consider:\\n\\n1. **Business income must be subject to self-employment tax**: Self-employment tax applies to net earnings from self-employment, which includes income from a trade or business. If you have income that is not subject to self-employment tax, such as interest, dividends, or capital gains, you cannot deduct business expenses related to that income.\\n2. **Business use percentage**: To qualify for the deduction, the business expense must be directly related to the production of income that is subject to self-employment tax. You can only deduct a portion of your total business expenses based on the amount of time spent working in the trade or business. This is known as the \"business use percentage.\"\\n3. **Business use percentage calculation**: To calculate the business use percentage, you\\'ll need to keep accurate records of your business and personal activities. You can use methods such as:\\n\\t* Time tracking: Record the number of hours worked on business versus personal activities.\\n\\t* Logbook or journal: Keep a log of business-related activities, including dates, times, and purposes.\\n\\t* Mileage log: If you drive for business, keep track of miles driven for business purposes.\\n4. **Deduction limits**: The IRS allows self-employed individuals to deduct business expenses up to the amount of their net earnings from self-employment. This means that if your net earnings are $100,000, and you have $50,000 in business expenses, you can only deduct up to $50,000.\\n\\nExamples of business expenses that may be deductible for income exempt from self-employment tax include:\\n\\n* Rent or mortgage interest (if used for a home office)\\n* Utilities (electricity, gas, water, internet)\\n* Office supplies\\n* Travel expenses related to business activities\\n* Meals and entertainment (subject to certain limits)\\n\\nHowever, some expenses are not deductible, such as:\\n\\n* Personal use of your car (unless you have a dedicated business vehicle)\\n* Home improvements or renovations\\n* Business use of personal phone or computer\\n\\nIt\\'s essential to keep accurate records and consult with a tax professional to ensure you\\'re taking advantage of the deductions you\\'re eligible for.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'As a tax preparer, I\\'d be happy to help clarify how self-employed individuals can report their income from a business with both self-employment income and income that is exempt from self-employment tax.\\n\\nSelf-employment income includes earnings from a trade or business, such as freelancing, consulting, or running a small business. However, not all self-employment income is subject to self-employment tax. Some types of income are exempt from self-employment tax, such as:\\n\\n1. Income earned by employees who are considered employees for tax purposes (e.g., W-2 income)\\n2. Income earned by individuals with disabilities or certain medical conditions that prevent them from working\\n3. Income earned by individuals who are retired or disabled and receiving Social Security benefits\\n\\nTo report self-employment income, the individual must complete Form 1040 and Schedule C (Form 1040), which is used to report business income and expenses. The self-employed individual will report their total net earnings from self-employment on Line 1 of Schedule C.\\n\\nHowever, if some of the self-employment income is exempt from self-employment tax, it\\'s essential to report that income separately. Here are a few scenarios:\\n\\nScenario 1: Exempt income is not subject to self-employment tax\\n\\nIf the business has both self-employment income and exempt income, the individual should report the exempt income on their tax return as ordinary income on Line 21 of Form 1040. The self-employment income will still be reported on Schedule C.\\n\\nScenario 2: Exempt income is subject to self-employment tax\\n\\nIf the business has both self-employment income and exempt income that is subject to self-employment tax, the individual should report the exempt income as \"Other Income\" on Line 21 of Form 1040. The self-employment income will still be reported on Schedule C.\\n\\nScenario 3: Exempt income is not subject to self-employment tax, but it\\'s also not ordinary income\\n\\nIf the business has both self-employment income and exempt income that are not subject to self-employment tax, the individual should report the exempt income as \"Other Income\" on Line 21 of Form 1040. The self-employment income will still be reported on Schedule C.\\n\\nIn all cases, the individual must also complete Schedule SE (Form 1040), which is used to calculate and pay self-employment tax. However, if some of the exempt income is not subject to self-employment tax, the individual may not need to pay self-employment tax on that amount.\\n\\nIt\\'s essential for self-employed individuals to keep accurate records of their business income and expenses to ensure they accurately report their income and claim any applicable deductions. It\\'s also recommended that they consult with a tax professional or accountant to ensure compliance with all tax laws and regulations.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"font-weight: bold\">]</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">scores</span>=<span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'braintrust::answer-similarity'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ScoringResult</span><span style=\"font-weight: bold\">(</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">aggregated_results</span>=<span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'average'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'average'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.4899263859389534</span><span style=\"font-weight: bold\">}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">score_rows</span>=<span style=\"font-weight: bold\">[</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5540326316427405</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6107129438872975</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6295656173500133</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6621756465647113</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7251324334585492</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6580514616988463</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.679013668656233</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6443694159054953</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6534822247099343</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6060499995255393</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6707352238393781</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5844465262881663</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6193049787006669</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.19265334618395002</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.3475911229721721</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.37030823883470115</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.25236308267577573</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5402693248940148</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5971543063171332</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.4717556066495579</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5813241919626898</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.42594780058940307</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.3775577464216217</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5752785957156418</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.4928045325528636</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6130954353884036</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5731572219578517</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.2721622295062875</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.4909561413127072</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.43785619682763427</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.43196526476505026</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.48082666644275657</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.3871573389983647</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5141049206455494</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.15621815507500153</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.23346143409633255</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5233557444748452</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.584189246942877</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.39744129545413726</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.423957948569605</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.11441727054056215</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.49638560386493197</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.4140458125149959</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"font-weight: bold\">]</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">)</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"font-weight: bold\">}</span>\n",
              "<span style=\"font-weight: bold\">)</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we have the results show that the native Llama3.2 3B instruct model got the avg score of 0.4899 on the tax Q&A eval dataset. Let's see if we can boost the LLM performance with post training."
      ],
      "metadata": {
        "id": "YWUpjf83Eoy-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Start Post Training\n",
        "Currenty, Llama stack post training APIs support [Supervised Fine-tune](https://cameronrwolfe.substack.com/p/understanding-and-using-supervised) which is a straightfoard and effective way to boost model performance on specific tasks.\n",
        "\n",
        "We start from [LoRA finetune algorithm](https://pytorch.org/torchtune/main/tutorials/lora_finetune.html#what-is-lora) that can significantly reduce finetune GPU memory usage as well as needs less data\n",
        "\n",
        "\n",
        "#### 2.0. Download the base model\n",
        "Download the Llama model that will be used with [the downloading model CLI](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html).\n",
        "\n",
        "Since ollama takes huggingface safetensor format checkpoint, we need to output the finetuned checkpoint in hugging face format. We download the model checkpoint from huggingface source.\n",
        "\n",
        "> You need to get a huggingface token from [here](https://huggingface.co/) and replace the \"HF_TOKEN\"\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "RWa220T5sjbR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!llama download --source huggingface --model-id Llama3.2-3B-Instruct --hf-token \"HF_TOKEN\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "yF50MtwcsogU",
        "outputId": "92ba3b3a-63a0-4ab8-c8cd-5437365128fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".gitattributes: 100% 1.52k/1.52k [00:00<00:00, 12.1MB/s]\n",
            "LICENSE.txt: 100% 7.71k/7.71k [00:00<00:00, 33.3MB/s]\n",
            "README.md: 100% 41.7k/41.7k [00:00<00:00, 56.9MB/s]\n",
            "USE_POLICY.md: 100% 6.02k/6.02k [00:00<00:00, 32.4MB/s]\n",
            "config.json: 100% 878/878 [00:00<00:00, 6.94MB/s]\n",
            "generation_config.json: 100% 189/189 [00:00<00:00, 1.71MB/s]\n",
            "model.safetensors.index.json: 100% 20.9k/20.9k [00:00<00:00, 87.0MB/s]\n",
            "consolidated.00.pth: 100% 6.43G/6.43G [00:18<00:00, 353MB/s]\n",
            "original%2Forig_params.json: 100% 220/220 [00:00<00:00, 1.69MB/s]\n",
            "original%2Fparams.json: 100% 220/220 [00:00<00:00, 1.64MB/s]\n",
            "tokenizer.model: 100% 2.18M/2.18M [00:00<00:00, 44.8MB/s]\n",
            "special_tokens_map.json: 100% 296/296 [00:00<00:00, 2.69MB/s]\n",
            "tokenizer.json: 100% 9.09M/9.09M [00:01<00:00, 8.57MB/s]\n",
            "tokenizer_config.json: 100% 54.5k/54.5k [00:00<00:00, 172MB/s]\n",
            "\n",
            "Successfully downloaded model to /root/.llama/checkpoints/Llama3.2-3B-Instruct\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####  2.1. Prepare post training dataset\n",
        "Llama stack supports 2 post training dataset formats (instruct and dialog), you can select which dataset format to use in step 2.1.\n",
        "- instruct dataset:\n",
        "  - schema:\n",
        "      - chat_completion_input: string (list of UserMessage, the length of the list is 1)\n",
        "      - expected_answer: string\n",
        "  - this format is the abstract of single-turn QA style dataset. During training, tokenized chat_completion_input + expected_answer will be model input, expected_answer will be label to calculate loss\n",
        "  - [example](https://gist.github.com/SLR722/b4ae7c8b05a0ea1a067e5262eb137ee2)\n",
        "\n",
        "- dialog dataset\n",
        "  - schema:\n",
        "      - dialog: string (list of interleaved UserMessages and AssistantMessages)\n",
        "  - this format is the abstract of multi-turn chat style dataset. During training, tokenized UserMessage content + AssistantMessage content + UserMessage content + AssistantMessage content ... concat together will be model input, AssistantMessage contents in the list will be label to calculate loss\n",
        "  - [example](https://gist.github.com/SLR722/20b3929032bc3a94cce3b8cc57788216)\n",
        "\n",
        "\n",
        " - Example scripts of converting json format dataset to llama stack format dataset ([to_llama_stack_dataset_instruct.py](https://gist.github.com/SLR722/3a76491190ce3225be935cc63c5332e6), [to_llama_stack_dataset_dialog.py](https://gist.github.com/SLR722/89dd6e41fab4505c327bd3fa99ea2f54))\n",
        "\n",
        "\n",
        "\n",
        "In our tax preparer example, we prepared a tax Q&A training dataset with synthetic data from Llama 3.3 70B model [tax_preparation_train.csv](https://gist.github.com/SLR722/49a8ce78fc705c0437523d3625c29b5d) (data source: https://github.com/shadi-fsai/modeluniversity/blob/main/trainable_data.json), which has no overlap with eval dataset.\n",
        "\n",
        "Since the tax Q&A dataset is single round Q&A, we use intruct dataset format for the post training.\n",
        "\n",
        "> **Note:** if you hit the input schema issue, you probably need to restart the runtime to apply your fix"
      ],
      "metadata": {
        "id": "V-Qa34Cfs62p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "\n",
        "# Upload the example dataset from github to notebook\n",
        "url = 'https://gist.githubusercontent.com/SLR722/49a8ce78fc705c0437523d3625c29b5d/raw/045f05be9cb6ebd5171fbdfce3306644ee435469/tax_preparation_train.csv'\n",
        "r = requests.get(url)\n",
        "with open('tax_preparation_train.csv', 'wb') as f:\n",
        "    f.write(r.content)\n",
        "\n",
        "# You can use the below comment out code to upload your local file to the notebook\n",
        "# from google.colab import files\n",
        "\n",
        "# uploaded = files.upload()"
      ],
      "metadata": {
        "id": "LfodcomxE8L0",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import mimetypes\n",
        "import base64\n",
        "\n",
        "# encode the dataset file into data_url\n",
        "def data_url_from_file(file_path: str) -> str:\n",
        "    if not os.path.exists(file_path):\n",
        "        raise FileNotFoundError(f\"File not found: {file_path}\")\n",
        "\n",
        "    with open(file_path, \"rb\") as file:\n",
        "        file_content = file.read()\n",
        "\n",
        "    base64_content = base64.b64encode(file_content).decode(\"utf-8\")\n",
        "    mime_type, _ = mimetypes.guess_type(file_path)\n",
        "\n",
        "    data_url = f\"data:{mime_type};base64,{base64_content}\"\n",
        "\n",
        "    return data_url\n",
        "\n",
        "data_url = data_url_from_file(\"tax_preparation_train.csv\")\n",
        "\n",
        "# register post training dataset\n",
        "# use the below commented out version for dialog dataset\n",
        "response = client.datasets.register(\n",
        "    dataset_id=\"post_training_dataset\",\n",
        "    provider_id=\"localfs\",\n",
        "    url={\"uri\": data_url},\n",
        "    dataset_schema={\n",
        "        \"chat_completion_input\": {\"type\": \"chat_completion_input\"},\n",
        "        \"expected_answer\": {\"type\": \"string\"},\n",
        "    },\n",
        ")\n",
        "\n",
        "# response = client.datasets.register(\n",
        "#     dataset_id=\"post_training_dataset\",\n",
        "#     provider_id=\"localfs\",\n",
        "#     url={\"uri\": data_url},\n",
        "#     dataset_schema={\n",
        "#         \"dialog\": {\"type\": \"dialog\"},\n",
        "#     },\n",
        "# )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "u57t43GVvRxp",
        "outputId": "ae119422-b7f8-473f-b6a7-049a0d0e5e22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2m00:42:16.035\u001b[0m \u001b[35m[START]\u001b[0m \u001b[2m/v1/datasets\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.2. Kick-off Post Training Job\n",
        "\n",
        "You can find the definition of post-training configs and APIs [here for server side](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/apis/post_training/post_training.py) and [here for client side](https://github.com/meta-llama/llama-stack-client-python/blob/d6f3ef24b740c996b29c0540bc6b4e996de0a168/src/llama_stack_client/types/post_training_supervised_fine_tune_params.py).\n",
        "\n",
        "> **Noet**: If you meet 'Job xxx already exists' error, you may also want to check the error logging above it. Since we have retry logic, the 'Job xxx already exists' may not be the root cause of the job failure"
      ],
      "metadata": {
        "id": "TodEWXXfMgg8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "S_VcSOR3Cng6",
        "outputId": "cbd6e62a-3dd1-4423-a11b-b06fd990e357",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "DEBUG:torchtune.utils._logging:Setting manual seed to local seed 28602197. Local seed is seed + rank = 28602197 + 0\n",
            "INFO:torchtune.utils._logging:Identified model_type = Llama3_2. Ignoring output.weight in checkpoint in favor of the tok_embedding.weight tied weights.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2m00:43:22.604\u001b[0m \u001b[35m[START]\u001b[0m \u001b[2m/v1/post-training/supervised-fine-tune\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.07 GiB\n",
            "\tGPU peak memory reserved: 6.11 GiB\n",
            "\tGPU peak memory active: 6.07 GiB\n",
            "INFO:llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:Model is initialized with precision torch.bfloat16.\n",
            "INFO:llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:Tokenizer is initialized.\n",
            "INFO:llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:Optimizer is initialized.\n",
            "INFO:llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:Loss is initialized.\n",
            "INFO:llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:Dataset and Sampler are initialized.\n",
            "INFO:llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:Learning rate scheduler is initialized.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing logs to /root/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0/log/log_1740530605.txt\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "1|1|Loss: 1.389875888824463:   1%|          | 1/153 [00:02<06:02,  2.38s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.30 GiB\n",
            "\tGPU peak memory reserved: 6.47 GiB\n",
            "\tGPU peak memory active: 6.30 GiB\n",
            "1|2|Loss: 1.416195273399353:   1%|▏         | 2/153 [00:03<04:24,  1.75s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.47 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|3|Loss: 1.5175566673278809:   2%|▏         | 3/153 [00:05<03:54,  1.56s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.30 GiB\n",
            "\tGPU peak memory reserved: 6.50 GiB\n",
            "\tGPU peak memory active: 6.30 GiB\n",
            "1|4|Loss: 1.463149905204773:   3%|▎         | 4/153 [00:06<03:55,  1.58s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.50 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|5|Loss: 1.5004178285598755:   3%|▎         | 5/153 [00:07<03:39,  1.48s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.50 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|6|Loss: 1.4015085697174072:   4%|▍         | 6/153 [00:09<03:28,  1.42s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.50 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|7|Loss: 1.062164306640625:   5%|▍         | 7/153 [00:10<03:21,  1.38s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.39 GiB\n",
            "\tGPU peak memory reserved: 6.59 GiB\n",
            "\tGPU peak memory active: 6.39 GiB\n",
            "1|8|Loss: 1.0587937831878662:   5%|▌         | 8/153 [00:11<03:16,  1.35s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.59 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|9|Loss: 0.8707118630409241:   6%|▌         | 9/153 [00:13<03:12,  1.34s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.59 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|10|Loss: 0.934844434261322:   7%|▋         | 10/153 [00:14<03:10,  1.33s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.59 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|11|Loss: 0.5765369534492493:   7%|▋         | 11/153 [00:15<03:09,  1.33s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.59 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|12|Loss: 0.5665200352668762:   8%|▊         | 12/153 [00:17<03:09,  1.34s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.59 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|13|Loss: 0.9018248319625854:   8%|▊         | 13/153 [00:18<03:06,  1.33s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.59 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|14|Loss: 0.7411351203918457:   9%|▉         | 14/153 [00:20<03:15,  1.41s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.59 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|15|Loss: 0.6155295968055725:  10%|▉         | 15/153 [00:21<03:09,  1.37s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.59 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|16|Loss: 0.493266224861145:  10%|█         | 16/153 [00:22<03:04,  1.34s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.38 GiB\n",
            "\tGPU peak memory reserved: 6.59 GiB\n",
            "\tGPU peak memory active: 6.38 GiB\n",
            "1|17|Loss: 0.5416454076766968:  11%|█         | 17/153 [00:23<03:00,  1.33s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.39 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.39 GiB\n",
            "1|18|Loss: 0.3843832015991211:  12%|█▏        | 18/153 [00:25<02:56,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|19|Loss: 0.3686770796775818:  12%|█▏        | 19/153 [00:26<02:54,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.38 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.38 GiB\n",
            "1|20|Loss: 0.6095303893089294:  13%|█▎        | 20/153 [00:27<02:54,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.31 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.31 GiB\n",
            "1|21|Loss: 0.5651540756225586:  14%|█▎        | 21/153 [00:29<02:52,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|22|Loss: 0.6179099678993225:  14%|█▍        | 22/153 [00:30<02:50,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.30 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.30 GiB\n",
            "1|23|Loss: 0.6599283814430237:  15%|█▌        | 23/153 [00:31<02:49,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|24|Loss: 0.8584531545639038:  16%|█▌        | 24/153 [00:33<02:58,  1.38s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|25|Loss: 0.551238477230072:  16%|█▋        | 25/153 [00:34<02:53,  1.36s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.38 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.38 GiB\n",
            "1|26|Loss: 0.4976871907711029:  17%|█▋        | 26/153 [00:35<02:49,  1.33s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.40 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.40 GiB\n",
            "1|27|Loss: 0.4901215136051178:  18%|█▊        | 27/153 [00:37<02:46,  1.32s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|28|Loss: 0.8195552229881287:  18%|█▊        | 28/153 [00:38<02:44,  1.32s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|29|Loss: 0.678187906742096:  19%|█▉        | 29/153 [00:39<02:42,  1.31s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|30|Loss: 0.6036797165870667:  20%|█▉        | 30/153 [00:41<02:40,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|31|Loss: 0.5398596525192261:  20%|██        | 31/153 [00:42<02:39,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|32|Loss: 0.4251810312271118:  21%|██        | 32/153 [00:43<02:36,  1.29s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|33|Loss: 0.3050590455532074:  22%|██▏       | 33/153 [00:44<02:35,  1.29s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|34|Loss: 0.3176429271697998:  22%|██▏       | 34/153 [00:46<02:43,  1.37s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|35|Loss: 0.4153244197368622:  23%|██▎       | 35/153 [00:47<02:39,  1.35s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|36|Loss: 0.4479702115058899:  24%|██▎       | 36/153 [00:49<02:35,  1.33s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|37|Loss: 0.7258309721946716:  24%|██▍       | 37/153 [00:50<02:33,  1.32s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.30 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.30 GiB\n",
            "1|38|Loss: 0.5819525718688965:  25%|██▍       | 38/153 [00:51<02:31,  1.32s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|39|Loss: 0.4619458019733429:  25%|██▌       | 39/153 [00:52<02:29,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|40|Loss: 0.45938149094581604:  26%|██▌       | 40/153 [00:54<02:27,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|41|Loss: 0.5025387406349182:  27%|██▋       | 41/153 [00:55<02:26,  1.31s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|42|Loss: 0.5231192708015442:  27%|██▋       | 42/153 [00:56<02:24,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|43|Loss: 0.6389061212539673:  28%|██▊       | 43/153 [00:58<02:23,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.31 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.31 GiB\n",
            "1|44|Loss: 0.5473061800003052:  29%|██▉       | 44/153 [00:59<02:30,  1.38s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|45|Loss: 0.6991505026817322:  29%|██▉       | 45/153 [01:00<02:26,  1.35s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.38 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.38 GiB\n",
            "1|46|Loss: 1.0255436897277832:  30%|███       | 46/153 [01:02<02:23,  1.34s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|47|Loss: 0.7800906300544739:  31%|███       | 47/153 [01:03<02:20,  1.33s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|48|Loss: 0.4505065381526947:  31%|███▏      | 48/153 [01:04<02:18,  1.32s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|49|Loss: 0.40819260478019714:  32%|███▏      | 49/153 [01:06<02:16,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.38 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.38 GiB\n",
            "1|50|Loss: 0.5696099400520325:  33%|███▎      | 50/153 [01:07<02:14,  1.31s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|51|Loss: 0.38793236017227173:  33%|███▎      | 51/153 [01:08<02:12,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|52|Loss: 0.3722645044326782:  34%|███▍      | 52/153 [01:10<02:10,  1.29s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|53|Loss: 0.5195285677909851:  35%|███▍      | 53/153 [01:11<02:09,  1.29s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|54|Loss: 0.5725739598274231:  35%|███▌      | 54/153 [01:12<02:07,  1.29s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|55|Loss: 0.673192024230957:  36%|███▌      | 55/153 [01:14<02:15,  1.38s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|56|Loss: 0.7062821388244629:  37%|███▋      | 56/153 [01:15<02:11,  1.35s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|57|Loss: 0.5854002833366394:  37%|███▋      | 57/153 [01:16<02:08,  1.34s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.30 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.30 GiB\n",
            "1|58|Loss: 0.83232182264328:  38%|███▊      | 58/153 [01:18<02:06,  1.33s/it]  INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|59|Loss: 0.49313250184059143:  39%|███▊      | 59/153 [01:19<02:04,  1.32s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.30 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.30 GiB\n",
            "1|60|Loss: 0.5762008428573608:  39%|███▉      | 60/153 [01:20<02:01,  1.31s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|61|Loss: 0.5938363671302795:  40%|███▉      | 61/153 [01:21<01:59,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.42 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.42 GiB\n",
            "1|62|Loss: 0.5302813649177551:  41%|████      | 62/153 [01:23<01:58,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.42 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.42 GiB\n",
            "1|63|Loss: 0.36335229873657227:  41%|████      | 63/153 [01:24<01:57,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.31 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.31 GiB\n",
            "1|64|Loss: 0.43203070759773254:  42%|████▏     | 64/153 [01:25<01:55,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|65|Loss: 0.38781753182411194:  42%|████▏     | 65/153 [01:27<01:54,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|66|Loss: 0.3424179255962372:  43%|████▎     | 66/153 [01:28<02:00,  1.38s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|67|Loss: 0.3940255045890808:  44%|████▍     | 67/153 [01:30<01:56,  1.35s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.38 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.38 GiB\n",
            "1|68|Loss: 0.27809983491897583:  44%|████▍     | 68/153 [01:31<01:52,  1.33s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|69|Loss: 0.49830225110054016:  45%|████▌     | 69/153 [01:32<01:50,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.41 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.41 GiB\n",
            "1|70|Loss: 0.643068790435791:  46%|████▌     | 70/153 [01:33<01:48,  1.31s/it]  INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|71|Loss: 0.6227353811264038:  46%|████▋     | 71/153 [01:35<01:46,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|72|Loss: 0.6814686059951782:  47%|████▋     | 72/153 [01:36<01:45,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|73|Loss: 0.57694411277771:  48%|████▊     | 73/153 [01:37<01:43,  1.29s/it]  INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|74|Loss: 0.4418116807937622:  48%|████▊     | 74/153 [01:39<01:42,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|75|Loss: 0.4225577116012573:  49%|████▉     | 75/153 [01:40<01:48,  1.39s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.31 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.31 GiB\n",
            "1|76|Loss: 0.5488865971565247:  50%|████▉     | 76/153 [01:41<01:44,  1.36s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|77|Loss: 0.5864394307136536:  50%|█████     | 77/153 [01:43<01:41,  1.34s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|78|Loss: 0.40422365069389343:  51%|█████     | 78/153 [01:44<01:39,  1.33s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|79|Loss: 0.5294312238693237:  52%|█████▏    | 79/153 [01:45<01:37,  1.32s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.31 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.31 GiB\n",
            "1|80|Loss: 0.604332685470581:  52%|█████▏    | 80/153 [01:47<01:35,  1.31s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|81|Loss: 0.7324197888374329:  53%|█████▎    | 81/153 [01:48<01:34,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|82|Loss: 0.6390347480773926:  54%|█████▎    | 82/153 [01:49<01:32,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|83|Loss: 0.43546730279922485:  54%|█████▍    | 83/153 [01:51<01:31,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|84|Loss: 0.481366366147995:  55%|█████▍    | 84/153 [01:52<01:29,  1.30s/it]  INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|85|Loss: 0.37979817390441895:  56%|█████▌    | 85/153 [01:53<01:28,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.30 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.30 GiB\n",
            "1|86|Loss: 0.5217821598052979:  56%|█████▌    | 86/153 [01:55<01:32,  1.38s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|87|Loss: 0.5387100577354431:  57%|█████▋    | 87/153 [01:56<01:29,  1.35s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|88|Loss: 0.5492819547653198:  58%|█████▊    | 88/153 [01:57<01:26,  1.33s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|89|Loss: 0.42111456394195557:  58%|█████▊    | 89/153 [01:59<01:24,  1.32s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|90|Loss: 0.4442729949951172:  59%|█████▉    | 90/153 [02:00<01:22,  1.31s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|91|Loss: 0.6047455668449402:  59%|█████▉    | 91/153 [02:01<01:21,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.31 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.31 GiB\n",
            "1|92|Loss: 0.5723249316215515:  60%|██████    | 92/153 [02:02<01:19,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|93|Loss: 0.5749974846839905:  61%|██████    | 93/153 [02:04<01:18,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|94|Loss: 0.5213482975959778:  61%|██████▏   | 94/153 [02:05<01:16,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|95|Loss: 0.5755754113197327:  62%|██████▏   | 95/153 [02:06<01:15,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.38 GiB\n",
            "\tGPU peak memory reserved: 6.70 GiB\n",
            "\tGPU peak memory active: 6.38 GiB\n",
            "1|96|Loss: 0.5397436022758484:  63%|██████▎   | 96/153 [02:08<01:18,  1.38s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.42 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.42 GiB\n",
            "1|97|Loss: 0.5803767442703247:  63%|██████▎   | 97/153 [02:09<01:15,  1.35s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.41 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.41 GiB\n",
            "1|98|Loss: 0.5896880626678467:  64%|██████▍   | 98/153 [02:10<01:13,  1.34s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.41 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.41 GiB\n",
            "1|99|Loss: 0.414295494556427:  65%|██████▍   | 99/153 [02:12<01:11,  1.32s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|100|Loss: 0.5486166477203369:  65%|██████▌   | 100/153 [02:13<01:09,  1.32s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.38 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.38 GiB\n",
            "1|101|Loss: 0.6509461998939514:  66%|██████▌   | 101/153 [02:14<01:08,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|102|Loss: 0.5313403010368347:  67%|██████▋   | 102/153 [02:16<01:06,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.28 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.28 GiB\n",
            "1|103|Loss: 0.5009002685546875:  67%|██████▋   | 103/153 [02:17<01:05,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.29 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.29 GiB\n",
            "1|104|Loss: 0.5051255822181702:  68%|██████▊   | 104/153 [02:18<01:03,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.28 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.28 GiB\n",
            "1|105|Loss: 0.5307162404060364:  69%|██████▊   | 105/153 [02:20<01:02,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.31 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.31 GiB\n",
            "1|106|Loss: 0.567254900932312:  69%|██████▉   | 106/153 [02:21<01:04,  1.38s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.38 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.38 GiB\n",
            "1|107|Loss: 0.5127613544464111:  70%|██████▉   | 107/153 [02:22<01:02,  1.35s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|108|Loss: 0.5610513687133789:  71%|███████   | 108/153 [02:24<01:00,  1.33s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|109|Loss: 0.5873624682426453:  71%|███████   | 109/153 [02:25<00:58,  1.32s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.40 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.40 GiB\n",
            "1|110|Loss: 0.529508113861084:  72%|███████▏  | 110/153 [02:26<00:56,  1.32s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.43 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.43 GiB\n",
            "1|111|Loss: 0.5214949250221252:  73%|███████▎  | 111/153 [02:28<00:54,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|112|Loss: 0.4938042163848877:  73%|███████▎  | 112/153 [02:29<00:53,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|113|Loss: 0.6205558180809021:  74%|███████▍  | 113/153 [02:30<00:52,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|114|Loss: 0.7692945599555969:  75%|███████▍  | 114/153 [02:32<00:50,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|115|Loss: 0.4957321882247925:  75%|███████▌  | 115/153 [02:33<00:49,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|116|Loss: 0.5726144909858704:  76%|███████▌  | 116/153 [02:34<00:51,  1.38s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|117|Loss: 0.38987457752227783:  76%|███████▋  | 117/153 [02:36<00:48,  1.35s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|118|Loss: 0.7716270685195923:  77%|███████▋  | 118/153 [02:37<00:46,  1.34s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|119|Loss: 0.5909061431884766:  78%|███████▊  | 119/153 [02:38<00:45,  1.34s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|120|Loss: 0.6103097796440125:  78%|███████▊  | 120/153 [02:40<00:43,  1.33s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|121|Loss: 0.4341275095939636:  79%|███████▉  | 121/153 [02:41<00:42,  1.32s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|122|Loss: 0.5941766500473022:  80%|███████▉  | 122/153 [02:42<00:40,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|123|Loss: 0.6706868410110474:  80%|████████  | 123/153 [02:44<00:39,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|124|Loss: 0.543195366859436:  81%|████████  | 124/153 [02:45<00:38,  1.31s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|125|Loss: 0.4078485369682312:  82%|████████▏ | 125/153 [02:46<00:36,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|126|Loss: 0.40242457389831543:  82%|████████▏ | 126/153 [02:47<00:35,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|127|Loss: 0.4367714822292328:  83%|████████▎ | 127/153 [02:49<00:36,  1.39s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|128|Loss: 0.601476788520813:  84%|████████▎ | 128/153 [02:50<00:34,  1.37s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|129|Loss: 0.5973384976387024:  84%|████████▍ | 129/153 [02:52<00:32,  1.35s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|130|Loss: 0.45393282175064087:  85%|████████▍ | 130/153 [02:53<00:30,  1.34s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|131|Loss: 0.58685702085495:  86%|████████▌ | 131/153 [02:54<00:29,  1.33s/it]   INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.38 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.38 GiB\n",
            "1|132|Loss: 0.6006588339805603:  86%|████████▋ | 132/153 [02:56<00:27,  1.32s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|133|Loss: 0.692461371421814:  87%|████████▋ | 133/153 [02:57<00:26,  1.32s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|134|Loss: 0.538910448551178:  88%|████████▊ | 134/153 [02:58<00:25,  1.32s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|135|Loss: 0.5809863805770874:  88%|████████▊ | 135/153 [02:59<00:23,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.34 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.34 GiB\n",
            "1|136|Loss: 0.48912352323532104:  89%|████████▉ | 136/153 [03:01<00:22,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|137|Loss: 0.6276236772537231:  90%|████████▉ | 137/153 [03:02<00:22,  1.40s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|138|Loss: 0.5042337775230408:  90%|█████████ | 138/153 [03:04<00:20,  1.37s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|139|Loss: 0.5499956607818604:  91%|█████████ | 139/153 [03:05<00:18,  1.35s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|140|Loss: 0.5758291482925415:  92%|█████████▏| 140/153 [03:06<00:17,  1.34s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|141|Loss: 0.6556288003921509:  92%|█████████▏| 141/153 [03:08<00:15,  1.33s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.35 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.35 GiB\n",
            "1|142|Loss: 0.643462598323822:  93%|█████████▎| 142/153 [03:09<00:14,  1.32s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.30 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.30 GiB\n",
            "1|143|Loss: 0.630422830581665:  93%|█████████▎| 143/153 [03:10<00:13,  1.32s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.29 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.29 GiB\n",
            "1|144|Loss: 0.5909254550933838:  94%|█████████▍| 144/153 [03:12<00:11,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|145|Loss: 0.4878236949443817:  95%|█████████▍| 145/153 [03:13<00:10,  1.32s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.39 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.39 GiB\n",
            "1|146|Loss: 0.45532599091529846:  95%|█████████▌| 146/153 [03:14<00:09,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|147|Loss: 0.4959859251976013:  96%|█████████▌| 147/153 [03:16<00:08,  1.39s/it] INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.37 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.37 GiB\n",
            "1|148|Loss: 0.6393123269081116:  97%|█████████▋| 148/153 [03:17<00:06,  1.36s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.39 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.39 GiB\n",
            "1|149|Loss: 0.5090091228485107:  97%|█████████▋| 149/153 [03:18<00:05,  1.34s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.33 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.33 GiB\n",
            "1|150|Loss: 0.5190550088882446:  98%|█████████▊| 150/153 [03:20<00:03,  1.33s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.31 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.31 GiB\n",
            "1|151|Loss: 0.9075320959091187:  99%|█████████▊| 151/153 [03:21<00:02,  1.32s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.31 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.31 GiB\n",
            "1|152|Loss: 0.7958194017410278:  99%|█████████▉| 152/153 [03:22<00:01,  1.31s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.36 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.36 GiB\n",
            "1|153|Loss: 0.7165011167526245: 100%|██████████| 153/153 [03:24<00:00,  1.30s/it]INFO:torchtune.utils._logging:Memory stats after model init:\n",
            "\tGPU peak memory allocation: 6.38 GiB\n",
            "\tGPU peak memory reserved: 6.82 GiB\n",
            "\tGPU peak memory active: 6.38 GiB\n",
            "INFO:llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:Starting checkpoint save...\n",
            "INFO:torchtune.utils._logging:Adapter checkpoint of size 0.02 GiB saved to /root/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0/adapter_model.pt\n",
            "INFO:torchtune.utils._logging:Adapter checkpoint of size 0.02 GiB saved to /root/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0/adapter/adapter_model.safetensors\n",
            "INFO:torchtune.utils._logging:Adapter checkpoint of size 0.00 GiB saved to /root/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0/adapter/adapter_config.json\n",
            "INFO:llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:Starting validation...\n",
            "\n",
            "  0%|          | 0/614 [00:00<?, ?it/s]\u001b[A\n",
            "  0%|          | 1/614 [00:00<01:06,  9.17it/s]\u001b[A\n",
            "validation step: 0:   0%|          | 1/614 [00:00<01:06,  9.17it/s]\u001b[A\n",
            "validation step: 0:   0%|          | 2/614 [00:00<01:08,  8.97it/s]\u001b[A\n",
            "validation step: 1:   0%|          | 2/614 [00:00<01:08,  8.97it/s]\u001b[A\n",
            "validation step: 1:   0%|          | 3/614 [00:00<01:08,  8.94it/s]\u001b[A\n",
            "validation step: 2:   0%|          | 3/614 [00:00<01:08,  8.94it/s]\u001b[A\n",
            "validation step: 2:   1%|          | 4/614 [00:00<01:08,  8.90it/s]\u001b[A\n",
            "validation step: 3:   1%|          | 4/614 [00:00<01:08,  8.90it/s]\u001b[A\n",
            "validation step: 3:   1%|          | 5/614 [00:00<01:08,  8.85it/s]\u001b[A\n",
            "validation step: 4:   1%|          | 5/614 [00:00<01:08,  8.85it/s]\u001b[A\n",
            "validation step: 4:   1%|          | 6/614 [00:00<01:08,  8.89it/s]\u001b[A\n",
            "validation step: 5:   1%|          | 6/614 [00:00<01:08,  8.89it/s]\u001b[A\n",
            "validation step: 5:   1%|          | 7/614 [00:00<01:08,  8.90it/s]\u001b[A\n",
            "validation step: 6:   1%|          | 7/614 [00:00<01:08,  8.90it/s]\u001b[A\n",
            "validation step: 6:   1%|▏         | 8/614 [00:00<01:08,  8.89it/s]\u001b[A\n",
            "validation step: 7:   1%|▏         | 8/614 [00:00<01:08,  8.89it/s]\u001b[A\n",
            "validation step: 7:   1%|▏         | 9/614 [00:01<02:03,  4.92it/s]\u001b[A\n",
            "validation step: 8:   1%|▏         | 9/614 [00:01<02:03,  4.92it/s]\u001b[A\n",
            "validation step: 8:   2%|▏         | 10/614 [00:01<01:46,  5.67it/s]\u001b[A\n",
            "validation step: 9:   2%|▏         | 10/614 [00:01<01:46,  5.67it/s]\u001b[A/usr/local/lib/python3.11/dist-packages/llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py:584: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  perplexity = torch.exp(torch.tensor(mean_loss))\n",
            "validation step: 9:   2%|▏         | 10/614 [00:01<01:26,  7.02it/s]\n",
            "1|153|Loss: 0.7165011167526245: 100%|██████████| 153/153 [03:39<00:00,  1.43s/it]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1;35mPostTrainingJob\u001b[0m\u001b[1m(\u001b[0m\u001b[33mjob_uuid\u001b[0m=\u001b[32m'1234'\u001b[0m\u001b[1m)\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">PostTrainingJob</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">job_uuid</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'1234'</span><span style=\"font-weight: bold\">)</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from llama_stack_client.types.post_training_supervised_fine_tune_params import (\n",
        "    TrainingConfig,\n",
        "    TrainingConfigDataConfig,\n",
        "    TrainingConfigEfficiencyConfig,\n",
        "    TrainingConfigOptimizerConfig,\n",
        ")\n",
        "from llama_stack_client.types.algorithm_config_param import LoraFinetuningConfig\n",
        "from rich.pretty import pprint\n",
        "\n",
        "algorithm_config = LoraFinetuningConfig(\n",
        "    type=\"LoRA\",\n",
        "    # List of which linear layers LoRA should be applied to in each self-attention block\n",
        "    # Options are {\"q_proj\", \"k_proj\", \"v_proj\", \"output_proj\"}.\n",
        "    lora_attn_modules=[\"q_proj\", \"v_proj\", \"output_proj\"],\n",
        "    # Whether to apply LoRA to the MLP in each transformer layer. Default: False\n",
        "    apply_lora_to_mlp=True,\n",
        "    # Whether to apply LoRA to the model's final output projection. Default: False\n",
        "    apply_lora_to_output=False,\n",
        "    # Rank of each low-rank approximation\n",
        "    rank=8,\n",
        "    # Scaling factor for the low-rank approximation\n",
        "    alpha=16,\n",
        ")\n",
        "\n",
        "data_config = TrainingConfigDataConfig(\n",
        "    # Identifier of the registered dataset for finetune\n",
        "    # Use client.datasets.list() to check all the available datasets\n",
        "    dataset_id=\"post_training_dataset\",\n",
        "    # Identifier of the registered dataset to validate the finetune model\n",
        "    # on validation_loss and perplexity\n",
        "    # Skip this if you don't want to run validatation on the model\n",
        "    validation_dataset_id=\"post_training_dataset\",\n",
        "    # Training data batch size\n",
        "    batch_size=2,\n",
        "    # Whether to shuffle the dataset.\n",
        "    shuffle=False,\n",
        "    # dataset format, select from ['instruct', 'dialog']\n",
        "    # change it to 'dialog' if you use dialog format dataset\n",
        "    data_format='instruct',\n",
        ")\n",
        "optimizer_config = TrainingConfigOptimizerConfig(\n",
        "    # Currently only support adamw\n",
        "    optimizer_type=\"adamw\",\n",
        "    # Learning rate\n",
        "    lr=3e-4,\n",
        "    # adamw weight decay coefficient\n",
        "    weight_decay=0.1,\n",
        "    # The number of steps for the warmup phase for lr scheduler\n",
        "    num_warmup_steps=10,\n",
        ")\n",
        "effiency_config = TrainingConfigEfficiencyConfig(\n",
        "    # Help reduce memory by recalculating some intermediate activations\n",
        "    # during backward\n",
        "    enable_activation_checkpointing=True,\n",
        "    # We offer another memory efficiency flag called enable_activation_offloading\n",
        "    # which moves certain activations from GPU memory to CPU memory\n",
        "    # This further reduces GPU memory usage at the cost of additional\n",
        "    # data transfer overhead and possible slowdowns\n",
        "    # enable_activation_offloading=False,\n",
        ")\n",
        "training_config = TrainingConfig(\n",
        "    # num of training epochs\n",
        "    n_epochs=1,\n",
        "    data_config=data_config,\n",
        "    efficiency_config=effiency_config,\n",
        "    optimizer_config=optimizer_config,\n",
        "    # max num of training steps per epoch\n",
        "    max_steps_per_epoch=10000,\n",
        "    # max num of steps for validation\n",
        "    max_validation_steps=10,\n",
        "    # Accumulate how many steps to calculate the gradient and update model parameters\n",
        "    # This is to simulate large batch size training while memory is limited\n",
        "    gradient_accumulation_steps=4,\n",
        ")\n",
        "\n",
        "# call supervised finetune API\n",
        "training_job = client.post_training.supervised_fine_tune(\n",
        "    job_uuid=\"1234\",\n",
        "    # Base Llama model to be finetuned on\n",
        "    model=\"meta-llama/Llama-3.2-3B-Instruct\",\n",
        "    algorithm_config=algorithm_config,\n",
        "    training_config=training_config,\n",
        "    # Base model checkpoint dir\n",
        "    # By default, the implementation will look at ~/.llama/checkpoints/<model>\n",
        "    checkpoint_dir=\"null\",\n",
        "    # logger_config and hyperparam_search_config haven't been supported yet\n",
        "    logger_config={},\n",
        "    hyperparam_search_config={},\n",
        ")\n",
        "\n",
        "pprint(training_job)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.3. list all the post training jobs"
      ],
      "metadata": {
        "id": "odNNDN9OMBOQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "wRFLJMEWu-eD",
        "outputId": "427d86bb-0acd-451f-ba51-80f7e3c5241b",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2m00:48:43.629\u001b[0m \u001b[35m[START]\u001b[0m \u001b[2m/v1/post-training/jobs\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m[\u001b[0m\u001b[1;35mData\u001b[0m\u001b[1m(\u001b[0m\u001b[33mjob_uuid\u001b[0m=\u001b[32m'1234'\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">[</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Data</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">job_uuid</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'1234'</span><span style=\"font-weight: bold\">)]</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "job_list = client.post_training.job.list()\n",
        "pprint(job_list)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.4. query the job status of a given post training job\n",
        "finetuned checkpoint metadata (validation metrics are included if available) and job metadata are provided in the status"
      ],
      "metadata": {
        "id": "3url0GUVMLo8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "-1sQe6QUzl_N",
        "outputId": "79145591-fbb4-425f-9bda-34e8eb6e356b",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2m00:49:06.134\u001b[0m \u001b[35m[START]\u001b[0m \u001b[2m/v1/post-training/job/status\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1;35mJobStatusResponse\u001b[0m\u001b[1m(\u001b[0m\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mcheckpoints\u001b[0m=\u001b[1m[\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'identifier'\u001b[0m: \u001b[32m'meta-llama/Llama-3.2-3B-Instruct-sft-0'\u001b[0m,\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'created_at'\u001b[0m: \u001b[32m'2025-02-26T00:46:58.602464'\u001b[0m,\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'epoch'\u001b[0m: \u001b[1;36m0\u001b[0m,\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'post_training_job_id'\u001b[0m: \u001b[32m'1234'\u001b[0m,\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'path'\u001b[0m: \u001b[32m'/root/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0'\u001b[0m,\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'training_metrics'\u001b[0m: \u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[32m'epoch'\u001b[0m: \u001b[1;36m0\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[32m'train_loss'\u001b[0m: \u001b[1;36m0.7165011167526245\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[32m'validation_loss'\u001b[0m: \u001b[1;36m0.3558155596256256\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[32m'perplexity'\u001b[0m: \u001b[1;36m1.4273443222045898\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[1m}\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m\n",
              "\u001b[2;32m│   \u001b[0m\u001b[1m]\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mjob_uuid\u001b[0m=\u001b[32m'1234'\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mstatus\u001b[0m=\u001b[32m'completed'\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mcompleted_at\u001b[0m=\u001b[1;35mdatetime\u001b[0m\u001b[1;35m.datetime\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m2025\u001b[0m, \u001b[1;36m2\u001b[0m, \u001b[1;36m26\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m47\u001b[0m, \u001b[1;36m4\u001b[0m, \u001b[1;36m901605\u001b[0m\u001b[1m)\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mresources_allocated\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mscheduled_at\u001b[0m=\u001b[1;35mdatetime\u001b[0m\u001b[1;35m.datetime\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m2025\u001b[0m, \u001b[1;36m2\u001b[0m, \u001b[1;36m26\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m43\u001b[0m, \u001b[1;36m22\u001b[0m, \u001b[1;36m601407\u001b[0m\u001b[1m)\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mstarted_at\u001b[0m=\u001b[1;35mdatetime\u001b[0m\u001b[1;35m.datetime\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m2025\u001b[0m, \u001b[1;36m2\u001b[0m, \u001b[1;36m26\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m43\u001b[0m, \u001b[1;36m22\u001b[0m, \u001b[1;36m777928\u001b[0m\u001b[1m)\u001b[0m\n",
              "\u001b[1m)\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">JobStatusResponse</span><span style=\"font-weight: bold\">(</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">checkpoints</span>=<span style=\"font-weight: bold\">[</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'identifier'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'meta-llama/Llama-3.2-3B-Instruct-sft-0'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'created_at'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'2025-02-26T00:46:58.602464'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'epoch'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'post_training_job_id'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'1234'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'path'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'/root/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'training_metrics'</span>: <span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'epoch'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'train_loss'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7165011167526245</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'validation_loss'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.3558155596256256</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'perplexity'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.4273443222045898</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"font-weight: bold\">}</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"font-weight: bold\">]</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">job_uuid</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'1234'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">status</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'completed'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">completed_at</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">datetime</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">.datetime</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2025</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">26</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">47</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">901605</span><span style=\"font-weight: bold\">)</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">resources_allocated</span>=<span style=\"font-weight: bold\">{}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">scheduled_at</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">datetime</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">.datetime</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2025</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">26</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">43</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">601407</span><span style=\"font-weight: bold\">)</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">started_at</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">datetime</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">.datetime</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2025</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">26</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">43</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">777928</span><span style=\"font-weight: bold\">)</span>\n",
              "<span style=\"font-weight: bold\">)</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "job_status = client.post_training.job.status(job_uuid='1234')\n",
        "pprint(job_status)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.5. get list of post training job artifacts (finetuned checkpoints)"
      ],
      "metadata": {
        "id": "5ARZ8cu-MgGf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "upIi2lebzuvL",
        "outputId": "479ca867-7660-4c51-edca-87bb472f1ccf",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2m00:49:12.609\u001b[0m \u001b[35m[START]\u001b[0m \u001b[2m/v1/post-training/job/artifacts\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1;35mJobArtifactsResponse\u001b[0m\u001b[1m(\u001b[0m\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mcheckpoints\u001b[0m=\u001b[1m[\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'identifier'\u001b[0m: \u001b[32m'meta-llama/Llama-3.2-3B-Instruct-sft-0'\u001b[0m,\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'created_at'\u001b[0m: \u001b[32m'2025-02-26T00:46:58.602464'\u001b[0m,\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'epoch'\u001b[0m: \u001b[1;36m0\u001b[0m,\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'post_training_job_id'\u001b[0m: \u001b[32m'1234'\u001b[0m,\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'path'\u001b[0m: \u001b[32m'/root/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0'\u001b[0m,\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'training_metrics'\u001b[0m: \u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[32m'epoch'\u001b[0m: \u001b[1;36m0\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[32m'train_loss'\u001b[0m: \u001b[1;36m0.7165011167526245\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[32m'validation_loss'\u001b[0m: \u001b[1;36m0.3558155596256256\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[32m'perplexity'\u001b[0m: \u001b[1;36m1.4273443222045898\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[1m}\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m\n",
              "\u001b[2;32m│   \u001b[0m\u001b[1m]\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mjob_uuid\u001b[0m=\u001b[32m'1234'\u001b[0m\n",
              "\u001b[1m)\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">JobArtifactsResponse</span><span style=\"font-weight: bold\">(</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">checkpoints</span>=<span style=\"font-weight: bold\">[</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'identifier'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'meta-llama/Llama-3.2-3B-Instruct-sft-0'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'created_at'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'2025-02-26T00:46:58.602464'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'epoch'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'post_training_job_id'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'1234'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'path'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'/root/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'training_metrics'</span>: <span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'epoch'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'train_loss'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7165011167526245</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'validation_loss'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.3558155596256256</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'perplexity'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.4273443222045898</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"font-weight: bold\">}</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"font-weight: bold\">]</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">job_uuid</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'1234'</span>\n",
              "<span style=\"font-weight: bold\">)</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "job_artifacts = client.post_training.job.artifacts(job_uuid='1234')\n",
        "pprint(job_artifacts)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Run Inference on the new model\n",
        "Woohoo! Now we have the new model finetuned on tax Q&A data ready! Now it's time to run inference to see some response from the model we just made!\n",
        "\n",
        "#### 3.0. Create a new model on ollama\n",
        "Please refer to [this doc](https://github.com/ollama/ollama/blob/main/docs/import.md) for more details on how to create a customized model from huggingface safetensor format adapter\n",
        "\n",
        "We need to launch xterm and enter the below commands\n",
        "\n",
        "\n",
        "```\n",
        "mkdir adapter\n",
        "\n",
        "# copy the adapter checkpoints of the finetuned model from Colab to xterm\n",
        "cp /root/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0/adapter/adapter_config.json ./adapter/\n",
        "cp /root/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0/adapter/adapter_model.safetensors ./adapter/\n",
        "\n",
        "# create a Modelfile file\n",
        "# You need to config the base model in FROM\n",
        "# and the path of adapter checkpoints in ADAPTER\n",
        "echo -e \"FROM llama3.2\\nADAPTER /content/adapter\" >> Modelfile\n",
        "\n",
        "# create the new model\n",
        "ollama create llama_3_2_finetuned\n",
        "ollama run llama_3_2_finetuned --keepalive 120m\n",
        "```\n",
        "\n",
        "> **TODO**: we plan to streamline this part by managing the finetuned checkpoints across post training and inference provider by /files API and put the above create customized model in ollama part with resigster_model method"
      ],
      "metadata": {
        "id": "uN2ha5mLDUZf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%xterm"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "https://localhost:10000/": {
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            "https://localhost:10000/in/G1syMDB+b2xsYW1hIHNlcnZlICYbWzIwMX4=": {
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          "base_uri": "https://localhost:8080/",
          "height": 839
        },
        "collapsed": true,
        "id": "fttIZbtzuxEF",
        "outputId": "c0d78f91-e37b-4c55-bb3e-d3549b7b44e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Launching Xterm..."
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        (async () => {\n",
              "            const url = new URL(await google.colab.kernel.proxyPort(10000, {'cache': true}));\n",
              "            const iframe = document.createElement('iframe');\n",
              "            iframe.src = url;\n",
              "            iframe.setAttribute('width', '100%');\n",
              "            iframe.setAttribute('height', '800');\n",
              "            iframe.setAttribute('frameborder', 0);\n",
              "            document.body.appendChild(iframe);\n",
              "        })();\n",
              "    "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "check if the finetuned model is running on ollama server successfully"
      ],
      "metadata": {
        "id": "-V6LC4vevKDb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!ollama ps"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "S6Ep70IZvOwW",
        "outputId": "555a2f11-dfd1-49be-cfa4-281ceca2aaf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NAME                          ID              SIZE      PROCESSOR    UNTIL            \n",
            "llama_3_2_finetuned:latest    a73e7ad20955    4.0 GB    100% GPU     2 hours from now    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.1. Register the new model"
      ],
      "metadata": {
        "id": "RbzBMdPFvRl-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "response = client.models.register(\n",
        "    # the model id here needs to be the finetuned checkpoint identifier\n",
        "    model_id=\"meta-llama/Llama-3.2-3B-Instruct-sft-0\",\n",
        "    provider_id=\"ollama\",\n",
        "    provider_model_id=\"llama_3_2_finetuned:latest\",\n",
        "    # base model id\n",
        "    metadata={\"llama_model\": \"meta-llama/Llama-3.2-3B-Instruct\"},\n",
        ")\n",
        "\n",
        "pprint(response)"
      ],
      "metadata": {
        "id": "M-HvLisYD3VR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "outputId": "fb1a2b79-d34d-4359-a879-c6e7b4d1ad27",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:httpx:HTTP Request: GET http://localhost:11434/api/ps \"HTTP/1.1 200 OK\"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2m00:53:05.319\u001b[0m \u001b[35m[START]\u001b[0m \u001b[2m/v1/models\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1;35mModel\u001b[0m\u001b[1m(\u001b[0m\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33midentifier\u001b[0m=\u001b[32m'meta-llama/Llama-3.2-3B-Instruct-sft-0'\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'llama_model'\u001b[0m: \u001b[32m'meta-llama/Llama-3.2-3B-Instruct'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mapi_model_type\u001b[0m=\u001b[32m'llm'\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mprovider_id\u001b[0m=\u001b[32m'ollama'\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mprovider_resource_id\u001b[0m=\u001b[32m'llama_3_2_finetuned:latest'\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'model'\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mmodel_type\u001b[0m=\u001b[32m'llm'\u001b[0m\n",
              "\u001b[1m)\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Model</span><span style=\"font-weight: bold\">(</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">identifier</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'meta-llama/Llama-3.2-3B-Instruct-sft-0'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">metadata</span>=<span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'llama_model'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'meta-llama/Llama-3.2-3B-Instruct'</span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">api_model_type</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'llm'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">provider_id</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'ollama'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">provider_resource_id</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'llama_3_2_finetuned:latest'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">type</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'model'</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">model_type</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'llm'</span>\n",
              "<span style=\"font-weight: bold\">)</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.2 Call the Llama stack [inference APIs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/apis/inference/inference.py) to run inference"
      ],
      "metadata": {
        "id": "Xz4sftAXEPQh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "response = client.inference.chat_completion(\n",
        "    model_id=\"meta-llama/Llama-3.2-3B-Instruct-sft-0\",\n",
        "    messages=[\n",
        "        {\"role\": \"user\", \"content\": \"What is the primary purpose of a W-2 form in relation to income tax?\"}\n",
        "    ],\n",
        ")\n",
        "\n",
        "print(response.completion_message.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fPSVBqfZEVr5",
        "outputId": "bdf2526b-9b02-4e58-a905-42bad8c501ae",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2m00:53:56.013\u001b[0m \u001b[35m[START]\u001b[0m \u001b[2m/v1/inference/chat-completion\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:httpx:HTTP Request: POST http://localhost:11434/api/generate \"HTTP/1.1 200 OK\"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "To report an employee's income and taxes withheld. My explanation: The W-2 form is used by employers to report an employee's income, taxes withheld, and other relevant information to the IRS.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Run evaluation on the finetuned checkpoints\n",
        "The finetuned checkpoint is naturally compatiable with Llama stack [eval APIs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/apis/eval/eval.py).\n",
        "\n",
        "Let's ru-run the evaluate sub-steps in step 1 to see if the post training gives us some meaningful improvments."
      ],
      "metadata": {
        "id": "yNwT7w3yM1y9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "I5SJ9P9f08lm",
        "outputId": "b52dbec3-3635-435d-a17a-84386494cbfb",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2m00:55:41.833\u001b[0m \u001b[35m[START]\u001b[0m \u001b[2m/v1/datasetio/rows\u001b[0m\n",
            "\u001b[2m00:55:41.833\u001b[0m \u001b[35m[END]\u001b[0m \u001b[2m/v1/datasetio/rows\u001b[0m\u001b[0m [StatusCode.OK]\u001b[0m (0.21ms)\n",
            "\u001b[2m00:55:41.848\u001b[0m \u001b[35m[START]\u001b[0m \u001b[2m/v1/eval/benchmarks\u001b[0m\n",
            "\u001b[2m00:55:41.858\u001b[0m \u001b[35m[END]\u001b[0m \u001b[2m/v1/eval/benchmarks\u001b[0m\u001b[0m [StatusCode.OK]\u001b[0m (9.47ms)\n",
            "\u001b[2m00:55:41.874\u001b[0m \u001b[35m[START]\u001b[0m \u001b[2m/v1/eval/benchmarks/Llama-3.2-3B-Instruct-sft-0:tax_eval/evaluations\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/43 [00:00<?, ?it/s]INFO:httpx:HTTP Request: POST http://localhost:11434/api/generate \"HTTP/1.1 200 OK\"\n",
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            " 86%|████████▌ | 37/43 [00:21<00:03,  1.90it/s]INFO:httpx:HTTP Request: POST http://localhost:11434/api/generate \"HTTP/1.1 200 OK\"\n",
            " 88%|████████▊ | 38/43 [00:22<00:03,  1.60it/s]INFO:httpx:HTTP Request: POST http://localhost:11434/api/generate \"HTTP/1.1 200 OK\"\n",
            " 91%|█████████ | 39/43 [00:22<00:02,  1.66it/s]INFO:httpx:HTTP Request: POST http://localhost:11434/api/generate \"HTTP/1.1 200 OK\"\n",
            " 93%|█████████▎| 40/43 [00:23<00:01,  1.63it/s]INFO:httpx:HTTP Request: POST http://localhost:11434/api/generate \"HTTP/1.1 200 OK\"\n",
            " 95%|█████████▌| 41/43 [00:24<00:01,  1.64it/s]INFO:httpx:HTTP Request: POST http://localhost:11434/api/generate \"HTTP/1.1 200 OK\"\n",
            " 98%|█████████▊| 42/43 [00:24<00:00,  1.53it/s]INFO:httpx:HTTP Request: POST http://localhost:11434/api/generate \"HTTP/1.1 200 OK\"\n",
            "100%|██████████| 43/43 [00:25<00:00,  1.68it/s]\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n",
            "INFO:httpx:HTTP Request: POST https://api.braintrust.dev/v1/proxy/embeddings \"HTTP/1.1 200 OK\"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1;35mEvaluateResponse\u001b[0m\u001b[1m(\u001b[0m\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mgenerations\u001b[0m=\u001b[1m[\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"To report an employee's income and taxes withheld.. My explanation: A W-2 form is used by employers to report an employee's income and taxes withheld, which are then reported on the employee's tax return.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'W-2 income is subject to federal and state income taxes, as well as other taxes such as Social Security and Medicare taxes.. My explanation: W-2 income is subject to various taxes, including federal and state income taxes, as well as Social Security and Medicare taxes.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Yes, W-2 income can be adjusted for tax purposes through various means, such as filing an amended return or claiming a refund.. My explanation: W-2 income can be adjusted for tax purposes through various means, such as filing an amended return or claiming a refund.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"Through the Social Security Administration and the Department of Veterans Affairs.. My explanation: The IRS verifies W-2 income through the Social Security Administration and the Department of Veterans Affairs, which can provide information on an individual's earnings history.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'W-2 income is reduced by pre-tax deductions, such as health insurance premiums and retirement contributions.. My explanation: Pre-tax deductions reduce W-2 income, which can impact tax liability and benefits eligibility.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Yes, if an employee has multiple jobs or is self-employed.. My explanation: If an individual has multiple jobs or is self-employed, they may have multiple W-2 forms to report their income and taxes withheld.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Tax credits can reduce W-2 income, which in turn reduces taxable income.. My explanation: Tax credits can reduce W-2 income, which in turn reduces taxable income and lowers the amount of taxes owed.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'W-2 income can trigger AMT if it exceeds certain thresholds.. My explanation: W-2 income can trigger the Alternative Minimum Tax \u001b[0m\u001b[32m(\u001b[0m\u001b[32mAMT\u001b[0m\u001b[32m)\u001b[0m\u001b[32m if it exceeds certain thresholds, which can result in additional tax liability.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'The TCJA reduced the top marginal tax rate from 39.6% to 37%. My explanation: The TCJA made significant changes to the tax code, including reducing the top marginal tax rate from 39.6% to 37%, which affects W-2 income.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'W-2 income is subject to NIIT, which can increase the overall tax liability.. My explanation: W-2 income is subject to NIIT, which can increase the overall tax liability.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'The ACA requires employers to provide health insurance coverage to employees, which can impact W-2 income.. My explanation: The ACA has changed the way employers report W-2 income, as they must now include information about health insurance coverage.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Self-Employment Tax is calculated based on net earnings from self-employment, which includes W-2 income.. My explanation: Self-Employment Tax is calculated based on net earnings from self-employment, which includes W-2 income.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'It is excluded from US taxable income, but may be subject to withholding and reporting requirements.. My explanation: The Foreign Earned Income Exclusion allows certain foreign earned income to be excluded from US taxable income. However, it may still be subject to withholding and reporting requirements.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Self-employment income, rent, and royalty income.. My explanation: Self-employment income, rent, and royalty income are typically reported on a 1099-MISC form.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Payers who have paid $600 or more in nonemployee compensation to an individual or entity.. My explanation: Payers must issue a 1099-MISC form to independent contractors if they pay $600 or more in nonemployee compensation.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'On Schedule C, which is attached to Form 1040.. My explanation: Self-employed individuals report their business expenses on Schedule C, which is attached to Form 1040. This schedule allows them to deduct business expenses and calculate their net profit or loss from self-employment.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'15.3% of net earnings from self-employment.. My explanation: The self-employment tax rate is 12.4% for Social Security and 2.9% for Medicare, for a total of 15.3%.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'By using Schedule C and calculating net earnings from self-employment. My explanation: Self-employed individuals calculate their self-employment tax by using Schedule C to determine their net earnings from self-employment, which is then used to calculate the self-employment tax liability.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Yes, but only 30% of the total expenses.. My explanation: Self-employed individuals can deduct business expenses related to their home office, but only up to 30% of the total expenses.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'By using Form 8829 and calculating the business use percentage based on the square footage used for business vs. personal use.. My explanation: Self-employed individuals can calculate the business use percentage of their home by using Form 8829, which requires them to calculate the business use percentage based on the square footage used for business vs. personal use.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'The Tax Cuts and Jobs Act \u001b[0m\u001b[32m(\u001b[0m\u001b[32mTCJA\u001b[0m\u001b[32m)\u001b[0m\u001b[32m limited the home office deduction to $5,000 per year for self-employed individuals and sole proprietors.. My explanation: The TCJA reduced the standard mileage rate from 58 cents to 58 cents per mile.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Yes, but only 50% of the cost.. My explanation: Self-employed individuals can deduct 50% of the cost of business meals on their tax return, subject to certain limits and requirements.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"On Schedule K-1. My explanation: Self-employed individuals report 1099 income from a partnership on Schedule K-1, which is used to report the partner's share of the partnership's income and expenses.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'The IRS may impose penalties and interest on unreported income.. My explanation: The IRS may impose penalties and interest on unreported income, including 1099 income, if it is not reported on a tax return.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Yes, but only 15.3% of net earnings from self-employment.. My explanation: Self-employed individuals can deduct half of their self-employment tax as an adjustment to income.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'They must file an amended return and provide documentation to support their income. My explanation: Self-employed individuals who receive a missing or incorrect 1099 form must file an amended return and provide documentation to support their income, such as bank records or other evidence of income.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Yes, but only within three years of filing their original return.. My explanation: Self-employed individuals can amend their tax return if they receive a corrected 1099 form, but they must do so within three years of filing their original return.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'January 31st of each year. My explanation: The IRS requires that employers provide employees with a 1099 form by January 31st of each year, showing the amount of money earned and taxes withheld.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'On Schedule C, Form 1040. My explanation: Self-employed individuals report 1099 income on Schedule C, which is used to calculate net earnings from self-employment and deduct business expenses.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Yes, but only for business-related expenses. My explanation: Self-employed individuals can deduct business expenses related to their business on Schedule C, but not personal expenses.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Using Schedule SE. My explanation: Self-employed individuals use Schedule SE to calculate their self-employment tax, which is used to fund Social Security and Medicare.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Self-employment income, including net earnings from self-employment.. My explanation: Self-employment income includes net earnings from self-employment, which can include income from a business or freelance work.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'On Schedule C, Form 1040. My explanation: Self-employed individuals report their business income and expenses on Schedule C, which is attached to Form 1040.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'15.3% of net earnings from self-employment.. My explanation: The self-employment tax rate is 12.4% for Social Security and 2.9% for Medicare, for a total of 15.3%.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Yes, but only 30% of the expenses.. My explanation: Self-employed individuals can deduct business use of their home as a business expense, but only 30% of the expenses are deductible.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'By subtracting business expenses and other deductions from gross income.. My explanation: Self-employed individuals must calculate their net earnings from self-employment by subtracting business expenses and other deductions from gross income.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"Yes, but only if they are not covered by their spouse's plan.. My explanation: Self-employed individuals can deduct health insurance premiums as a business expense, but only if they are not covered by their spouse's plan.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"A single-member LLC is taxed as a pass-through entity, while a sole proprietorship is not. My explanation: A single-member LLC is taxed as a pass-through entity, meaning that the business income is only reported on the owner's personal tax return, while a sole proprietorship is subject to self-employment taxes and is reported on the owner's personal tax return.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"On Schedule C, Form 1040. My explanation: Self-employed individuals who are partners in a business must report their share of the partnership's income on Schedule C, which is attached to Form 1040.\"\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Yes, but only if they are made to a SEP-IRA or other qualified retirement plans.. My explanation: Self-employed individuals can deduct retirement plan contributions as a business expense, but only if they are made to a SEP-IRA or other qualified retirement plans.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'By using Schedule C and calculating the net profit or loss from business activities. My explanation: Self-employed individuals can use Schedule C to calculate their net profit or loss from business activities, which is then used to determine self-employment tax liability.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Yes, but only if they meet certain requirements.. My explanation: Self-employed individuals can deduct business expenses related to the production of income that is exempt from self-employment tax, such as income from a rental property or a partnership. However, these expenses must be ordinary and necessary for the production of the income.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'On Schedule C, with the non-self-employment income reported on Form 1040. My explanation: Self-employed individuals must report all income from their business, including income that is exempt from self-employment tax, on Schedule C and then report it on Form 1040.'\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m}\u001b[0m\n",
              "\u001b[2;32m│   \u001b[0m\u001b[1m]\u001b[0m,\n",
              "\u001b[2;32m│   \u001b[0m\u001b[33mscores\u001b[0m=\u001b[1m{\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[32m'braintrust::answer-similarity'\u001b[0m: \u001b[1;35mScoringResult\u001b[0m\u001b[1m(\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[33maggregated_results\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'average'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'average'\u001b[0m: \u001b[1;36m0.5802955570078431\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[33mscore_rows\u001b[0m=\u001b[1m[\u001b[0m\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6565447051087072\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.7172851928136957\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.7401882676556717\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6259443609703588\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.7113645084925231\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.7689447680897838\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.7324857089526651\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.7503574047565974\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6501787694446832\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5967525606780247\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.8209298935370634\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5750908327577023\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.610959594105671\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.27193564785511154\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5009250423255521\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.654372745652473\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.4049431408069166\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5120535121791207\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5796474371127261\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.531959990822166\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5838211393592547\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.7210106827316267\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6475723780816662\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5347988578097088\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6956716509909102\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5482922570324981\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.563191715384755\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.4153433637836649\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6473572616262823\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6287912046599122\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.3535854496760741\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6994224687039214\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6851640716483164\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6506213667228251\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.22177374319292117\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.22375849317599947\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5513696068095729\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6748749489066432\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.49861799411654095\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.4505224368297718\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.37972468499212686\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.6184272480032537\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
              "\u001b[2;32m│   │   │   │   \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.5461318429817944\u001b[0m, \u001b[32m'metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m\n",
              "\u001b[2;32m│   │   │   \u001b[0m\u001b[1m]\u001b[0m\n",
              "\u001b[2;32m│   │   \u001b[0m\u001b[1m)\u001b[0m\n",
              "\u001b[2;32m│   \u001b[0m\u001b[1m}\u001b[0m\n",
              "\u001b[1m)\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">EvaluateResponse</span><span style=\"font-weight: bold\">(</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">generations</span>=<span style=\"font-weight: bold\">[</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"To report an employee's income and taxes withheld.. My explanation: A W-2 form is used by employers to report an employee's income and taxes withheld, which are then reported on the employee's tax return.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'W-2 income is subject to federal and state income taxes, as well as other taxes such as Social Security and Medicare taxes.. My explanation: W-2 income is subject to various taxes, including federal and state income taxes, as well as Social Security and Medicare taxes.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Yes, W-2 income can be adjusted for tax purposes through various means, such as filing an amended return or claiming a refund.. My explanation: W-2 income can be adjusted for tax purposes through various means, such as filing an amended return or claiming a refund.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Through the Social Security Administration and the Department of Veterans Affairs.. My explanation: The IRS verifies W-2 income through the Social Security Administration and the Department of Veterans Affairs, which can provide information on an individual's earnings history.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'W-2 income is reduced by pre-tax deductions, such as health insurance premiums and retirement contributions.. My explanation: Pre-tax deductions reduce W-2 income, which can impact tax liability and benefits eligibility.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Yes, if an employee has multiple jobs or is self-employed.. My explanation: If an individual has multiple jobs or is self-employed, they may have multiple W-2 forms to report their income and taxes withheld.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Tax credits can reduce W-2 income, which in turn reduces taxable income.. My explanation: Tax credits can reduce W-2 income, which in turn reduces taxable income and lowers the amount of taxes owed.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'W-2 income can trigger AMT if it exceeds certain thresholds.. My explanation: W-2 income can trigger the Alternative Minimum Tax (AMT) if it exceeds certain thresholds, which can result in additional tax liability.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'The TCJA reduced the top marginal tax rate from 39.6% to 37%. My explanation: The TCJA made significant changes to the tax code, including reducing the top marginal tax rate from 39.6% to 37%, which affects W-2 income.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'W-2 income is subject to NIIT, which can increase the overall tax liability.. My explanation: W-2 income is subject to NIIT, which can increase the overall tax liability.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'The ACA requires employers to provide health insurance coverage to employees, which can impact W-2 income.. My explanation: The ACA has changed the way employers report W-2 income, as they must now include information about health insurance coverage.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Self-Employment Tax is calculated based on net earnings from self-employment, which includes W-2 income.. My explanation: Self-Employment Tax is calculated based on net earnings from self-employment, which includes W-2 income.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'It is excluded from US taxable income, but may be subject to withholding and reporting requirements.. My explanation: The Foreign Earned Income Exclusion allows certain foreign earned income to be excluded from US taxable income. However, it may still be subject to withholding and reporting requirements.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Self-employment income, rent, and royalty income.. My explanation: Self-employment income, rent, and royalty income are typically reported on a 1099-MISC form.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Payers who have paid $600 or more in nonemployee compensation to an individual or entity.. My explanation: Payers must issue a 1099-MISC form to independent contractors if they pay $600 or more in nonemployee compensation.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'On Schedule C, which is attached to Form 1040.. My explanation: Self-employed individuals report their business expenses on Schedule C, which is attached to Form 1040. This schedule allows them to deduct business expenses and calculate their net profit or loss from self-employment.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'15.3% of net earnings from self-employment.. My explanation: The self-employment tax rate is 12.4% for Social Security and 2.9% for Medicare, for a total of 15.3%.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'By using Schedule C and calculating net earnings from self-employment. My explanation: Self-employed individuals calculate their self-employment tax by using Schedule C to determine their net earnings from self-employment, which is then used to calculate the self-employment tax liability.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Yes, but only 30% of the total expenses.. My explanation: Self-employed individuals can deduct business expenses related to their home office, but only up to 30% of the total expenses.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'By using Form 8829 and calculating the business use percentage based on the square footage used for business vs. personal use.. My explanation: Self-employed individuals can calculate the business use percentage of their home by using Form 8829, which requires them to calculate the business use percentage based on the square footage used for business vs. personal use.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'The Tax Cuts and Jobs Act (TCJA) limited the home office deduction to $5,000 per year for self-employed individuals and sole proprietors.. My explanation: The TCJA reduced the standard mileage rate from 58 cents to 58 cents per mile.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Yes, but only 50% of the cost.. My explanation: Self-employed individuals can deduct 50% of the cost of business meals on their tax return, subject to certain limits and requirements.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"On Schedule K-1. My explanation: Self-employed individuals report 1099 income from a partnership on Schedule K-1, which is used to report the partner's share of the partnership's income and expenses.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'The IRS may impose penalties and interest on unreported income.. My explanation: The IRS may impose penalties and interest on unreported income, including 1099 income, if it is not reported on a tax return.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Yes, but only 15.3% of net earnings from self-employment.. My explanation: Self-employed individuals can deduct half of their self-employment tax as an adjustment to income.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'They must file an amended return and provide documentation to support their income. My explanation: Self-employed individuals who receive a missing or incorrect 1099 form must file an amended return and provide documentation to support their income, such as bank records or other evidence of income.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Yes, but only within three years of filing their original return.. My explanation: Self-employed individuals can amend their tax return if they receive a corrected 1099 form, but they must do so within three years of filing their original return.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'January 31st of each year. My explanation: The IRS requires that employers provide employees with a 1099 form by January 31st of each year, showing the amount of money earned and taxes withheld.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'On Schedule C, Form 1040. My explanation: Self-employed individuals report 1099 income on Schedule C, which is used to calculate net earnings from self-employment and deduct business expenses.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Yes, but only for business-related expenses. My explanation: Self-employed individuals can deduct business expenses related to their business on Schedule C, but not personal expenses.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Using Schedule SE. My explanation: Self-employed individuals use Schedule SE to calculate their self-employment tax, which is used to fund Social Security and Medicare.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Self-employment income, including net earnings from self-employment.. My explanation: Self-employment income includes net earnings from self-employment, which can include income from a business or freelance work.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'On Schedule C, Form 1040. My explanation: Self-employed individuals report their business income and expenses on Schedule C, which is attached to Form 1040.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'15.3% of net earnings from self-employment.. My explanation: The self-employment tax rate is 12.4% for Social Security and 2.9% for Medicare, for a total of 15.3%.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Yes, but only 30% of the expenses.. My explanation: Self-employed individuals can deduct business use of their home as a business expense, but only 30% of the expenses are deductible.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'By subtracting business expenses and other deductions from gross income.. My explanation: Self-employed individuals must calculate their net earnings from self-employment by subtracting business expenses and other deductions from gross income.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Yes, but only if they are not covered by their spouse's plan.. My explanation: Self-employed individuals can deduct health insurance premiums as a business expense, but only if they are not covered by their spouse's plan.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"A single-member LLC is taxed as a pass-through entity, while a sole proprietorship is not. My explanation: A single-member LLC is taxed as a pass-through entity, meaning that the business income is only reported on the owner's personal tax return, while a sole proprietorship is subject to self-employment taxes and is reported on the owner's personal tax return.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"On Schedule C, Form 1040. My explanation: Self-employed individuals who are partners in a business must report their share of the partnership's income on Schedule C, which is attached to Form 1040.\"</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Yes, but only if they are made to a SEP-IRA or other qualified retirement plans.. My explanation: Self-employed individuals can deduct retirement plan contributions as a business expense, but only if they are made to a SEP-IRA or other qualified retirement plans.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'By using Schedule C and calculating the net profit or loss from business activities. My explanation: Self-employed individuals can use Schedule C to calculate their net profit or loss from business activities, which is then used to determine self-employment tax liability.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Yes, but only if they meet certain requirements.. My explanation: Self-employed individuals can deduct business expenses related to the production of income that is exempt from self-employment tax, such as income from a rental property or a partnership. However, these expenses must be ordinary and necessary for the production of the income.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'On Schedule C, with the non-self-employment income reported on Form 1040. My explanation: Self-employed individuals must report all income from their business, including income that is exempt from self-employment tax, on Schedule C and then report it on Form 1040.'</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">}</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"font-weight: bold\">]</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">scores</span>=<span style=\"font-weight: bold\">{</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"color: #008000; text-decoration-color: #008000\">'braintrust::answer-similarity'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ScoringResult</span><span style=\"font-weight: bold\">(</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">aggregated_results</span>=<span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'average'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'average'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5802955570078431</span><span style=\"font-weight: bold\">}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">score_rows</span>=<span style=\"font-weight: bold\">[</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6565447051087072</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7172851928136957</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7401882676556717</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6259443609703588</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7113645084925231</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7689447680897838</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7324857089526651</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7503574047565974</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6501787694446832</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5967525606780247</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.8209298935370634</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5750908327577023</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.610959594105671</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.27193564785511154</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5009250423255521</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.654372745652473</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.4049431408069166</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5120535121791207</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5796474371127261</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.531959990822166</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5838211393592547</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7210106827316267</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6475723780816662</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5347988578097088</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6956716509909102</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5482922570324981</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.563191715384755</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.4153433637836649</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6473572616262823</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6287912046599122</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.3535854496760741</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6994224687039214</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6851640716483164</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6506213667228251</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.22177374319292117</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.22375849317599947</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5513696068095729</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6748749489066432</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.49861799411654095</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.4505224368297718</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.37972468499212686</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6184272480032537</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>,\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   │   </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5461318429817944</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>: <span style=\"font-weight: bold\">{}}</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   │   </span><span style=\"font-weight: bold\">]</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   │   </span><span style=\"font-weight: bold\">)</span>\n",
              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"font-weight: bold\">}</span>\n",
              "<span style=\"font-weight: bold\">)</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# We limit to 50 rows from the dataset to save time\n",
        "eval_rows = client.datasetio.get_rows_paginated(\n",
        "    dataset_id=\"eval_dataset\",\n",
        "    rows_in_page=-1,\n",
        ")\n",
        "\n",
        "from tqdm import tqdm\n",
        "\n",
        "\n",
        "system_message = {\n",
        "    \"role\": \"system\",\n",
        "    \"content\": \"You are a tax preparer.\",\n",
        "}\n",
        "\n",
        "client.benchmarks.register(\n",
        "    benchmark_id=\"Llama-3.2-3B-Instruct-sft-0:tax_eval\",\n",
        "    dataset_id=\"eval_dataset\",\n",
        "    scoring_functions=[\"braintrust::answer-similarity\"]\n",
        ")\n",
        "\n",
        "response = client.eval.evaluate_rows(\n",
        "    benchmark_id=\"Llama-3.2-3B-Instruct-sft-0:tax_eval\",\n",
        "    input_rows=eval_rows.rows,\n",
        "    scoring_functions=[\"braintrust::answer-similarity\"],\n",
        "    benchmark_config={\n",
        "        \"type\": \"benchmark\",\n",
        "        \"eval_candidate\": {\n",
        "            \"type\": \"model\",\n",
        "            \"model\": \"meta-llama/Llama-3.2-3B-Instruct-sft-0\",\n",
        "            \"sampling_params\": {\n",
        "                \"temperature\": 0.0,\n",
        "                \"max_tokens\": 4096,\n",
        "                \"top_p\": 0.9,\n",
        "                \"repeat_penalty\": 1.0,\n",
        "            },\n",
        "            \"system_message\": system_message\n",
        "        }\n",
        "    }\n",
        ")\n",
        "pprint(response)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Wow, you see? we are able to improve the eval score from 0.4899 to 0.5803 (**18.5% improvement**) with a ~1000 samples dataset and a few mintutes training on a single GPU!\n",
        "\n",
        "\n",
        "It's just a start. There are several tricks on parameters tuning, training dataset processing etc. to further boost the finetune performance for you to explore.\n",
        "\n",
        "Now, it's time to enhance your own agentic application with post training. Happy tuning!"
      ],
      "metadata": {
        "id": "XlPDvNdLWc83"
      }
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "A100",
      "provenance": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
