forked from phoenix/litellm-mirror
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13 KiB
Text
Vendored
404 lines
No EOL
13 KiB
Text
Vendored
{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
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"machine_shape": "hm",
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"gpuType": "V100"
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# Set up Environment"
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],
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"metadata": {
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"id": "vDOm5wfjdFLP"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"!pip install --upgrade litellm"
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],
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"metadata": {
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"id": "Bx6mAA6MHiy_"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "zIYv7JTyxSxR",
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"outputId": "53890320-f9fa-4bf4-8362-0f17f52c6ed4"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Successfully installed fastapi-0.103.1 h11-0.14.0 huggingface-hub-0.16.4 ninja-1.11.1 pydantic-1.10.12 ray-2.6.3 safetensors-0.3.3 sentencepiece-0.1.99 starlette-0.27.0 tokenizers-0.13.3 transformers-4.33.1 uvicorn-0.23.2 vllm-0.1.4 xformers-0.0.21\n"
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]
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}
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],
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"source": [
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"!pip install vllm"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Load the Logs"
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],
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"metadata": {
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"id": "RMcoAni6WKEx"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"import pandas as pd"
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],
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"metadata": {
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"id": "zchxB8c7WJe5"
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},
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"execution_count": 4,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# path of the csv file\n",
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"file_path = 'Model-prompts-example.csv'\n",
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"\n",
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"# load the csv file as a pandas DataFrame\n",
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"data = pd.read_csv(file_path)\n",
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"\n",
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"data.head()"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 81
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},
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"id": "aKcWr015WNPm",
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"outputId": "6e226773-333f-46a2-9fc8-4f54f309d204"
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},
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"execution_count": 6,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" Success Timestamp Input \\\n",
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"0 True 1694041195 This is the templated query input \n",
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"\n",
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" Output RunId (Wandb Runid) \\\n",
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"0 This is the query output from the model 8hlumwuk \n",
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"\n",
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" Model ID (or Name) \n",
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"0 OpenAI/Turbo-3.5 "
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],
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"text/html": [
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"\n",
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" <div id=\"df-cd06d09e-fb43-41b0-938f-37f9d285ae66\" class=\"colab-df-container\">\n",
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" <div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Success</th>\n",
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" <th>Timestamp</th>\n",
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" <th>Input</th>\n",
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" <th>Output</th>\n",
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" <th>RunId (Wandb Runid)</th>\n",
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" <th>Model ID (or Name)</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>True</td>\n",
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" <td>1694041195</td>\n",
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" <td>This is the templated query input</td>\n",
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" <td>This is the query output from the model</td>\n",
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" <td>8hlumwuk</td>\n",
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" <td>OpenAI/Turbo-3.5</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>\n",
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" <div class=\"colab-df-buttons\">\n",
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"\n",
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" <div class=\"colab-df-container\">\n",
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" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd06d09e-fb43-41b0-938f-37f9d285ae66')\"\n",
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" title=\"Convert this dataframe to an interactive table.\"\n",
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" style=\"display:none;\">\n",
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"\n",
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" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
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" </svg>\n",
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" </button>\n",
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"\n",
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" <style>\n",
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" .colab-df-container {\n",
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" display:flex;\n",
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" gap: 12px;\n",
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" }\n",
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"\n",
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" .colab-df-convert {\n",
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" background-color: #E8F0FE;\n",
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" border: none;\n",
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" border-radius: 50%;\n",
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" cursor: pointer;\n",
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" display: none;\n",
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" fill: #1967D2;\n",
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" height: 32px;\n",
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" padding: 0 0 0 0;\n",
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" width: 32px;\n",
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" }\n",
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"\n",
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" .colab-df-convert:hover {\n",
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" background-color: #E2EBFA;\n",
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" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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" fill: #174EA6;\n",
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" }\n",
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"\n",
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" .colab-df-buttons div {\n",
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" margin-bottom: 4px;\n",
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" }\n",
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"\n",
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" [theme=dark] .colab-df-convert {\n",
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" background-color: #3B4455;\n",
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" fill: #D2E3FC;\n",
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" }\n",
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"\n",
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" [theme=dark] .colab-df-convert:hover {\n",
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" background-color: #434B5C;\n",
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" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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" fill: #FFFFFF;\n",
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" }\n",
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" </style>\n",
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"\n",
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" <script>\n",
|
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" const buttonEl =\n",
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" document.querySelector('#df-cd06d09e-fb43-41b0-938f-37f9d285ae66 button.colab-df-convert');\n",
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" buttonEl.style.display =\n",
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" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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"\n",
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" async function convertToInteractive(key) {\n",
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" const element = document.querySelector('#df-cd06d09e-fb43-41b0-938f-37f9d285ae66');\n",
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" const dataTable =\n",
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" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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" [key], {});\n",
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" if (!dataTable) return;\n",
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"\n",
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" const docLinkHtml = 'Like what you see? Visit the ' +\n",
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" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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" + ' to learn more about interactive tables.';\n",
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" element.innerHTML = '';\n",
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" dataTable['output_type'] = 'display_data';\n",
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" await google.colab.output.renderOutput(dataTable, element);\n",
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" const docLink = document.createElement('div');\n",
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" docLink.innerHTML = docLinkHtml;\n",
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" element.appendChild(docLink);\n",
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" }\n",
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" </script>\n",
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" </div>\n",
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"\n",
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"\n",
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" </div>\n",
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" </div>\n"
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]
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},
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"metadata": {},
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"execution_count": 6
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"input_texts = data['Input'].values"
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],
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"metadata": {
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"id": "0DbL-kirWUyn"
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},
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"execution_count": 7,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"messages = [[{\"role\": \"user\", \"content\": input_text}] for input_text in input_texts]"
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],
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"metadata": {
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"id": "cqpAvy8hWXyC"
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},
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"execution_count": 8,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Running Inference"
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],
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"metadata": {
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"id": "SugCyom0Xy8U"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"from litellm import batch_completion\n",
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"model_name = \"facebook/opt-125m\"\n",
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"provider = \"vllm\"\n",
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"response_list = batch_completion(\n",
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" model=model_name,\n",
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" custom_llm_provider=provider, # can easily switch to huggingface, replicate, together ai, sagemaker, etc.\n",
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" messages=messages,\n",
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" temperature=0.2,\n",
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" max_tokens=80,\n",
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" )"
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],
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"metadata": {
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"id": "qpikx3uxHns3"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"response_list"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "QDPikHtwKJJ2",
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"outputId": "06f47c44-e258-452a-f9db-232a5b6d2810"
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},
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"execution_count": 10,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"[<ModelResponse at 0x7e5b87616750> JSON: {\n",
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" \"choices\": [\n",
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" {\n",
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" \"finish_reason\": \"stop\",\n",
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" \"index\": 0,\n",
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" \"message\": {\n",
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" \"content\": \".\\n\\nThe query input is the query input that is used to query the data.\\n\\nThe query input is the query input that is used to query the data.\\n\\nThe query input is the query input that is used to query the data.\\n\\nThe query input is the query input that is used to query the data.\\n\\nThe query input is the query input that is\",\n",
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" \"role\": \"assistant\",\n",
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" \"logprobs\": null\n",
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" }\n",
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" }\n",
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" ],\n",
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" \"created\": 1694053363.6139505,\n",
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" \"model\": \"facebook/opt-125m\",\n",
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" \"usage\": {\n",
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" \"prompt_tokens\": 9,\n",
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" \"completion_tokens\": 80,\n",
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" \"total_tokens\": 89\n",
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" }\n",
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" }]"
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]
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},
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"metadata": {},
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"execution_count": 10
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"response_values = [response['choices'][0]['message']['content'] for response in response_list]"
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],
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"metadata": {
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"id": "SYqTcCiJbQDF"
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},
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"execution_count": 11,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"response_values"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "wqs-Oy9FbiPo",
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"outputId": "16a6a7b7-97c8-4b5b-eff8-09ea5eb5ad06"
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},
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"execution_count": 12,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"['.\\n\\nThe query input is the query input that is used to query the data.\\n\\nThe query input is the query input that is used to query the data.\\n\\nThe query input is the query input that is used to query the data.\\n\\nThe query input is the query input that is used to query the data.\\n\\nThe query input is the query input that is']"
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]
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},
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"metadata": {},
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"execution_count": 12
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"data[f\"{model_name}_output\"] = response_values"
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],
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"metadata": {
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"id": "mElNbBehbkrz"
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},
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"execution_count": 13,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"data.to_csv('model_responses.csv', index=False)"
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],
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"metadata": {
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"id": "F06NXssDc45k"
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},
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"execution_count": 14,
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"outputs": []
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}
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]
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} |