mirror of
https://github.com/meta-llama/llama-stack.git
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c54164556a
The `--image-name __system__` thing was a hack and a bad one at that. The actual intent was to somehow automatically detect the notebook environment so we could avoid unnecessarily confusing things in the llama stack build cmd-line. But I failed which led us to use the backup `__system__` thing. Let's just do the simple thing. Note that `build_venv.sh` I haven't changed for now (so it still honors the __system__ special name just that no new user should use it.) ## Test Plan Open the notebooks from this branch in Colab (see example url below) and ensure the builds work. https://colab.research.google.com/github/meta-llama/llama-stack/blob/foo/docs/getting_started.ipynb In the notebook, install llama-stack from this branch directly using: ``` !pip install -U https://github.com/meta-llama/llama-stack/archive/refs/heads/foo.zip ``` Verify that `!UV_SYSTEM_PYTHON=1 llama stack build --template together --image-type venv` afterwards succeeds and the library client initialization also works.
4241 lines
192 KiB
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4241 lines
192 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "hTIfyoGtjoWD"
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},
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"source": [
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||
"[](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb)\n",
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||
"\n",
|
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"# Llama Stack Benchmark Evals\n",
|
||
"\n",
|
||
"This notebook will walk you through the main sets of APIs we offer with Llama Stack for supporting running benchmark evaluations of your with working examples to explore the possibilities that Llama Stack opens up for you.\n",
|
||
"\n",
|
||
"Read more about Llama Stack: https://llama-stack.readthedocs.io/en/latest/index.html"
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]
|
||
},
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||
{
|
||
"cell_type": "markdown",
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||
"metadata": {
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||
"id": "bxs0FJ1ckGa6"
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||
},
|
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"source": [
|
||
"## 0. Bootstrapping Llama Stack Library\n",
|
||
"\n",
|
||
"##### 0.1. Prerequisite: Create TogetherAI account\n",
|
||
"\n",
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||
"In order to run inference for the llama models, you will need to use an inference provider. Llama stack supports a number of inference [providers](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/remote/inference).\n",
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||
"\n",
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||
"In this showcase, we will use [together.ai](https://www.together.ai/) as the inference provider. So, you would first get an API key from Together if you dont have one already.\n",
|
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"You can also use Fireworks.ai or even Ollama if you would like to.\n",
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"\n",
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"\n",
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"> **Note:** Set the API Key in the Secrets of this notebook as `TOGETHER_API_KEY`"
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]
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},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
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||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
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||
},
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"collapsed": true,
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"id": "O9pGVlPIjpix",
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"outputId": "e1fbe723-ae31-4630-eb80-4c4f6476d56f"
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||
},
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"outputs": [],
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||
"source": [
|
||
"# NBVAL_SKIP\n",
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||
"!pip install -U llama-stack"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": null,
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"metadata": {
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||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
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||
"collapsed": true,
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||
"id": "JQpLUSNjlGAM",
|
||
"outputId": "2f7fec97-5511-4cae-d51e-6d262fbca19c"
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||
},
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"outputs": [],
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"source": [
|
||
"# NBVAL_SKIP\n",
|
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"!UV_SYSTEM_PYTHON=1 llama stack build --template together --image-type venv"
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||
]
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},
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{
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||
"cell_type": "code",
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"execution_count": 1,
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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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"collapsed": true,
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"id": "KkT2qVeTlI-b",
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"outputId": "9198fbfc-a126-4409-e2f5-5f5bf5cdf9a7"
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},
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"outputs": [
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||
{
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||
"name": "stdout",
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||
"output_type": "stream",
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||
"text": [
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"Not in Google Colab environment\n",
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"\u001b[33mWarning: `bwrap` is not available. Code interpreter tool will not work correctly.\u001b[0m\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/opt/anaconda3/envs/master/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
|
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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},
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{
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"data": {
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"text/html": [
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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\">Using config <span style=\"color: #000080; text-decoration-color: #000080\">together</span>:\n",
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"</pre>\n"
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],
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"text/plain": [
|
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"Using config \u001b[34mtogether\u001b[0m:\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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"text/html": [
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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\">apis:\n",
|
||
"- agents\n",
|
||
"- datasetio\n",
|
||
"- eval\n",
|
||
"- inference\n",
|
||
"- memory\n",
|
||
"- safety\n",
|
||
"- scoring\n",
|
||
"- telemetry\n",
|
||
"- tool_runtime\n",
|
||
"datasets: <span style=\"font-weight: bold\">[]</span>\n",
|
||
"container_image: null\n",
|
||
"benchmarks: <span style=\"font-weight: bold\">[]</span>\n",
|
||
"image_name: together\n",
|
||
"memory_banks: <span style=\"font-weight: bold\">[]</span>\n",
|
||
"metadata_store:\n",
|
||
" db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/Users/xiyan/.llama/distributions/together/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">registry.db</span>\n",
|
||
" namespace: null\n",
|
||
" type: sqlite\n",
|
||
"models:\n",
|
||
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
|
||
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-8B-Instruct\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - llm\n",
|
||
" provider_id: together\n",
|
||
" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-8B-Instruct-Turbo\n",
|
||
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
|
||
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-70B-Instruct\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - llm\n",
|
||
" provider_id: together\n",
|
||
" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-70B-Instruct-Turbo\n",
|
||
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
|
||
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-405B-Instruct-FP8\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - llm\n",
|
||
" provider_id: together\n",
|
||
" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-405B-Instruct-Turbo\n",
|
||
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
|
||
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-3B-Instruct\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - llm\n",
|
||
" provider_id: together\n",
|
||
" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-3B-Instruct-Turbo\n",
|
||
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
|
||
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-11B-Vision-Instruct\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - llm\n",
|
||
" provider_id: together\n",
|
||
" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-11B-Vision-Instruct-Turbo\n",
|
||
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
|
||
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-90B-Vision-Instruct\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - llm\n",
|
||
" provider_id: together\n",
|
||
" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-90B-Vision-Instruct-Turbo\n",
|
||
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
|
||
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.3</span>-70B-Instruct\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - llm\n",
|
||
" provider_id: together\n",
|
||
" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.3</span>-70B-Instruct-Turbo\n",
|
||
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
|
||
" model_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-8B\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - llm\n",
|
||
" provider_id: together\n",
|
||
" provider_model_id: meta-llama/Meta-Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-8B\n",
|
||
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
|
||
" model_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-11B-Vision\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - llm\n",
|
||
" provider_id: together\n",
|
||
" provider_model_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-11B-Vision-Turbo\n",
|
||
"- metadata:\n",
|
||
" embedding_dimension: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">384</span>\n",
|
||
" model_id: all-MiniLM-L6-v2\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - embedding\n",
|
||
" provider_id: sentence-transformers\n",
|
||
" provider_model_id: null\n",
|
||
"providers:\n",
|
||
" agents:\n",
|
||
" - config:\n",
|
||
" persistence_store:\n",
|
||
" db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/Users/xiyan/.llama/distributions/together/</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: huggingface\n",
|
||
" provider_type: remote::huggingface\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",
|
||
" api_key: <span style=\"color: #008000; text-decoration-color: #008000\">'********'</span>\n",
|
||
" url: <span style=\"color: #0000ff; text-decoration-color: #0000ff; text-decoration: underline\">https://api.together.xyz/v1</span>\n",
|
||
" provider_id: together\n",
|
||
" provider_type: remote::together\n",
|
||
" - config: <span style=\"font-weight: bold\">{}</span>\n",
|
||
" provider_id: sentence-transformers\n",
|
||
" provider_type: inline::sentence-transformers\n",
|
||
" memory:\n",
|
||
" - config:\n",
|
||
" kvstore:\n",
|
||
" db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/Users/xiyan/.llama/distributions/together/</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",
|
||
" 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: <span style=\"font-weight: bold\">{}</span>\n",
|
||
" provider_id: llm-as-judge\n",
|
||
" provider_type: inline::llm-as-judge\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:\n",
|
||
" service_name: llama-stack\n",
|
||
" sinks: sqlite\n",
|
||
" sqlite_db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/Users/xiyan/.llama/distributions/together/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">trace_store.db</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",
|
||
" - 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: tavily-search\n",
|
||
" provider_type: remote::tavily-search\n",
|
||
" - config: <span style=\"font-weight: bold\">{}</span>\n",
|
||
" provider_id: code-interpreter\n",
|
||
" provider_type: inlin<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">e::c</span>ode-interpreter\n",
|
||
" - config: <span style=\"font-weight: bold\">{}</span>\n",
|
||
" provider_id: rag-runtime\n",
|
||
" provider_type: inline::rag-runtime\n",
|
||
"scoring_fns: <span style=\"font-weight: bold\">[]</span>\n",
|
||
"shields:\n",
|
||
"- params: null\n",
|
||
" provider_id: null\n",
|
||
" provider_shield_id: null\n",
|
||
" shield_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-8B\n",
|
||
"tool_groups:\n",
|
||
"- args: null\n",
|
||
" mcp_endpoint: null\n",
|
||
" provider_id: tavily-search\n",
|
||
" toolgroup_id: builtin::websearch\n",
|
||
"- args: null\n",
|
||
" mcp_endpoint: null\n",
|
||
" provider_id: rag-runtime\n",
|
||
" toolgroup_id: builtin::rag\n",
|
||
"- args: null\n",
|
||
" mcp_endpoint: null\n",
|
||
" provider_id: code-interpreter\n",
|
||
" toolgroup_id: builtin::code_interpreter\n",
|
||
"version: <span style=\"color: #008000; text-decoration-color: #008000\">'2'</span>\n",
|
||
"\n",
|
||
"</pre>\n"
|
||
],
|
||
"text/plain": [
|
||
"apis:\n",
|
||
"- agents\n",
|
||
"- datasetio\n",
|
||
"- eval\n",
|
||
"- inference\n",
|
||
"- memory\n",
|
||
"- safety\n",
|
||
"- scoring\n",
|
||
"- telemetry\n",
|
||
"- tool_runtime\n",
|
||
"datasets: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
|
||
"container_image: null\n",
|
||
"benchmarks: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
|
||
"image_name: together\n",
|
||
"memory_banks: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
|
||
"metadata_store:\n",
|
||
" db_path: \u001b[35m/Users/xiyan/.llama/distributions/together/\u001b[0m\u001b[95mregistry.db\u001b[0m\n",
|
||
" namespace: null\n",
|
||
" type: sqlite\n",
|
||
"models:\n",
|
||
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
|
||
" model_id: meta-llama/Llama-\u001b[1;36m3.1\u001b[0m-8B-Instruct\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - llm\n",
|
||
" provider_id: together\n",
|
||
" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-8B-Instruct-Turbo\n",
|
||
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
|
||
" model_id: meta-llama/Llama-\u001b[1;36m3.1\u001b[0m-70B-Instruct\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - llm\n",
|
||
" provider_id: together\n",
|
||
" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-70B-Instruct-Turbo\n",
|
||
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
|
||
" model_id: meta-llama/Llama-\u001b[1;36m3.1\u001b[0m-405B-Instruct-FP8\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - llm\n",
|
||
" provider_id: together\n",
|
||
" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-405B-Instruct-Turbo\n",
|
||
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
|
||
" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-3B-Instruct\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - llm\n",
|
||
" provider_id: together\n",
|
||
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-3B-Instruct-Turbo\n",
|
||
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
|
||
" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-11B-Vision-Instruct\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - llm\n",
|
||
" provider_id: together\n",
|
||
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-11B-Vision-Instruct-Turbo\n",
|
||
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
|
||
" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-90B-Vision-Instruct\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - llm\n",
|
||
" provider_id: together\n",
|
||
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-90B-Vision-Instruct-Turbo\n",
|
||
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
|
||
" model_id: meta-llama/Llama-\u001b[1;36m3.3\u001b[0m-70B-Instruct\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - llm\n",
|
||
" provider_id: together\n",
|
||
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.3\u001b[0m-70B-Instruct-Turbo\n",
|
||
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
|
||
" model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - llm\n",
|
||
" provider_id: together\n",
|
||
" provider_model_id: meta-llama/Meta-Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
|
||
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
|
||
" model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-11B-Vision\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - llm\n",
|
||
" provider_id: together\n",
|
||
" provider_model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-11B-Vision-Turbo\n",
|
||
"- metadata:\n",
|
||
" embedding_dimension: \u001b[1;36m384\u001b[0m\n",
|
||
" model_id: all-MiniLM-L6-v2\n",
|
||
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
|
||
" - embedding\n",
|
||
" provider_id: sentence-transformers\n",
|
||
" provider_model_id: null\n",
|
||
"providers:\n",
|
||
" agents:\n",
|
||
" - config:\n",
|
||
" persistence_store:\n",
|
||
" db_path: \u001b[35m/Users/xiyan/.llama/distributions/together/\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: huggingface\n",
|
||
" provider_type: remote::huggingface\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",
|
||
" api_key: \u001b[32m'********'\u001b[0m\n",
|
||
" url: \u001b[4;94mhttps://api.together.xyz/v1\u001b[0m\n",
|
||
" provider_id: together\n",
|
||
" provider_type: remote::together\n",
|
||
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
|
||
" provider_id: sentence-transformers\n",
|
||
" provider_type: inline::sentence-transformers\n",
|
||
" memory:\n",
|
||
" - config:\n",
|
||
" kvstore:\n",
|
||
" db_path: \u001b[35m/Users/xiyan/.llama/distributions/together/\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",
|
||
" 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: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
|
||
" provider_id: llm-as-judge\n",
|
||
" provider_type: inline::llm-as-judge\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:\n",
|
||
" service_name: llama-stack\n",
|
||
" sinks: sqlite\n",
|
||
" sqlite_db_path: \u001b[35m/Users/xiyan/.llama/distributions/together/\u001b[0m\u001b[95mtrace_store.db\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",
|
||
" - config:\n",
|
||
" api_key: \u001b[32m'********'\u001b[0m\n",
|
||
" max_results: \u001b[1;36m3\u001b[0m\n",
|
||
" provider_id: tavily-search\n",
|
||
" provider_type: remote::tavily-search\n",
|
||
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
|
||
" provider_id: code-interpreter\n",
|
||
" provider_type: inlin\u001b[1;92me::c\u001b[0mode-interpreter\n",
|
||
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
|
||
" provider_id: rag-runtime\n",
|
||
" provider_type: inline::rag-runtime\n",
|
||
"scoring_fns: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
|
||
"shields:\n",
|
||
"- params: null\n",
|
||
" provider_id: null\n",
|
||
" provider_shield_id: null\n",
|
||
" shield_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
|
||
"tool_groups:\n",
|
||
"- args: null\n",
|
||
" mcp_endpoint: null\n",
|
||
" provider_id: tavily-search\n",
|
||
" toolgroup_id: builtin::websearch\n",
|
||
"- args: null\n",
|
||
" mcp_endpoint: null\n",
|
||
" provider_id: rag-runtime\n",
|
||
" toolgroup_id: builtin::rag\n",
|
||
"- args: null\n",
|
||
" mcp_endpoint: null\n",
|
||
" provider_id: code-interpreter\n",
|
||
" toolgroup_id: builtin::code_interpreter\n",
|
||
"version: \u001b[32m'2'\u001b[0m\n",
|
||
"\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import os\n",
|
||
"\n",
|
||
"try:\n",
|
||
" from google.colab import userdata\n",
|
||
" os.environ['TOGETHER_API_KEY'] = userdata.get('TOGETHER_API_KEY')\n",
|
||
" os.environ['TAVILY_SEARCH_API_KEY'] = userdata.get('TAVILY_SEARCH_API_KEY')\n",
|
||
"except ImportError:\n",
|
||
" print(\"Not in Google Colab environment\")\n",
|
||
"\n",
|
||
"from llama_stack.distribution.library_client import LlamaStackAsLibraryClient\n",
|
||
"\n",
|
||
"client = LlamaStackAsLibraryClient(\"together\")\n",
|
||
"_ = client.initialize()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "qwXHwHq4lS1s"
|
||
},
|
||
"source": [
|
||
"## 1. Open Benchmark Model Evaluation\n",
|
||
"\n",
|
||
"The first example walks you through how to evaluate a model candidate served by Llama Stack on open benchmarks. We will use the following benchmark:\n",
|
||
"\n",
|
||
"- [MMMU](https://arxiv.org/abs/2311.16502) (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI)]: Benchmark designed to evaluate multimodal models.\n",
|
||
"- [SimpleQA](https://openai.com/index/introducing-simpleqa/): Benchmark designed to access models to answer short, fact-seeking questions."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "dqXLFtcao1oI"
|
||
},
|
||
"source": [
|
||
"#### 1.1 Running MMMU\n",
|
||
"- We will use a pre-processed MMMU dataset from [llamastack/mmmu](https://huggingface.co/datasets/llamastack/mmmu). The preprocessing code is shown in in this [Github Gist](https://gist.github.com/yanxi0830/118e9c560227d27132a7fd10e2c92840). The dataset is obtained by transforming the original [MMMU/MMMU](https://huggingface.co/datasets/MMMU/MMMU) dataset into correct format by `inference/chat-completion` API."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {
|
||
"id": "TC_IwIAQo4q-"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"name = \"llamastack/mmmu\"\n",
|
||
"subset = \"Agriculture\"\n",
|
||
"split = \"dev\""
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 305,
|
||
"referenced_widgets": [
|
||
"feb82e061ee44283b4a46be858ef4cd7",
|
||
"78a2d2d4ee3f42f3be42ef4baa298561",
|
||
"ba5e6ca09f174ef3a348453cf5cfc24a",
|
||
"74b58e4647644c9daf9af488942fdaf4",
|
||
"d56e218958a041e286e80f24e400ab0b",
|
||
"cab80632b7564a9eb59583e09573c1ee",
|
||
"10c0d50d7c204de0b4c8e8f4d3ec0af5",
|
||
"626ef2f811ae4e119a0e85cebe92b91d",
|
||
"aef4172d916f40b0ab4ed09104e10f24",
|
||
"25529e7fd57049d2816d31f696eab1fd",
|
||
"093bdcb608cf4b4fa37b0032a3915187",
|
||
"c788d4e9e1e24dca9b6503689df9b631",
|
||
"d1587e2144bf46299c1bdec3ea96e4e7",
|
||
"500a072c09da41759cb2c942a16d8429",
|
||
"9785009392934e3bbb229e8781667cbc",
|
||
"84570fe2c2a54a068fb9b8cbc8b041a1",
|
||
"f9e579c58e3f4ae0bbb721dffa33bf0a",
|
||
"737116977f474ec0b68d88a40fd1086c",
|
||
"e6d6e516cd03452297d80c36376855dd",
|
||
"6ae0fadb3aeb4be18a9ab3279fb23145",
|
||
"fa4800a506ac480984d58933580df086",
|
||
"117468099dbc42fdaafc08207eaac7ab",
|
||
"44f585990aa244d8ba61f892dc1ccc1c",
|
||
"4fc59928a0544f95a4438b37d19ca437",
|
||
"fb644d47049f495397d0e60597c86ea3",
|
||
"78632694ff694442bc3fefc2cac2cbf5",
|
||
"083fd2549abd4b03bd41d8b92ec28f42",
|
||
"611d6472a58d419583acc416767a4c90",
|
||
"98c5ce434cff454eaaa3f0fd3498183a",
|
||
"3d0344a9cc744e369da1b6b7ea1b3be8",
|
||
"c452ccbf47a44073aee710175f707a7d",
|
||
"0218397c573e4b28bfb4ffa66464d50f",
|
||
"9b01bcd6e5174be2af19f457047017c8",
|
||
"4fed5720f30b4b3cbbc606a4f25e223b",
|
||
"6fa866b9971542739b0ed26d90ceac80",
|
||
"fe7553b513954cc68c427b5d9d260b33",
|
||
"4bc266d49a6741a88350e029d101425b",
|
||
"da57445f98e7427589962836c2b4287e",
|
||
"ad1fb86cc1f94fd9911eda03cf4a3783",
|
||
"fdefb51ad4c4418b98c5826126558011",
|
||
"179d41b80dc841e8a440482516b8bca5",
|
||
"22b1ecd2eff14770bcfb0c62d3d4213f",
|
||
"47f876cf41484d55b645e1e99337423a",
|
||
"340fbbb4982c460992c88885e79b47db",
|
||
"9659140487ca4d3ea799196d2c1ecf61",
|
||
"52150fd494d24eea89b5232077509355",
|
||
"04acde771d0a46699e1de07d9733d1a3",
|
||
"7b98103300814f3caea84266263b95a2",
|
||
"75f06408071c494f934bb909b84110d1",
|
||
"b09b2690894749339a9172e5ad0a9b75",
|
||
"cbed38801163438d891879b756f5baab",
|
||
"399a6417b23e4593bb244ec3abb6b46d",
|
||
"53a321f36b0d4e08a74a5bcfbd04434b",
|
||
"b8c0c8aaac0d4032bf5c673a43d084ab",
|
||
"d1f32499fa3f4795b92361637e23a9bb",
|
||
"c06f9a090fb54c74b947634bf6d11fa8",
|
||
"82991dcc80f14af9bd2e95f705980676",
|
||
"cd832e3842b945aabbb327856053f261",
|
||
"93ee645d54f34acdb0d15092d4a6f0d1",
|
||
"b77fe05bbcf84cdc8ef85b264ccd35f6",
|
||
"e17d286a965a49cfb8d5bf885865cb1e",
|
||
"ca015c1a0c1449e68edb282462435a3f",
|
||
"2932b06afde9468a976eb6bfb072b80e",
|
||
"d027c807ddc04f89bec41dc05fde7718",
|
||
"4ff3a6aaf706460bbba01b248b93000e",
|
||
"bfd75a39f0154c30adbaad1e2ca0f1e2",
|
||
"4f788a7920c346f3b42900825bd6711a",
|
||
"8e9358ec7d474808bb96c13e13489c67",
|
||
"f0dfeee2a8d64dedbc8ef55ad4e69932",
|
||
"9437b707bf1a4847a50aafeb4252dab5",
|
||
"f255707788704a76bd1651f26a22402d",
|
||
"3b70fa4e43ef4951862e119378c3c501",
|
||
"6c0a6a7fa8ca4e1c961a36305f0e7638",
|
||
"201bd914f9884e46b8e6df9d9900a6e8",
|
||
"f53b7ada01084e73bba6e14a95e2a534",
|
||
"d2029292327b488db02fd123ee2b75af",
|
||
"3e26bc24a3e44b4582f57913bdf98de4",
|
||
"9d2b6eabf7e14436b72bbf374b4a2a0a",
|
||
"b5d7cb5a6157449a850ef0e12e3d3eb7",
|
||
"c245d316bf9e44dabe5bfd1e47fc8d2e",
|
||
"963cf422ca894d82b0dd94c6165d41bf",
|
||
"78d0e2aa93674bbeb42bff87a23cce9b",
|
||
"12c6f1180eeb4e9eb9037ea5dd24ec8e",
|
||
"017a81d7160240a398947545963856f5",
|
||
"1cf8eeb8d81c4e8a8e95dd43296a78b9",
|
||
"5b0b5a3f79e94c51aae48fe0dd34ba0e",
|
||
"f5b34a743ce54fb591f25b04a2651d65",
|
||
"dec6399e2c5341aead66e1674d3e6c72",
|
||
"24e48376a72940679989a39a40bbe7f6",
|
||
"484df732051540859bc7ac9cecadc83c",
|
||
"4b33b1db50c34a2fa957d81a71a2a47f",
|
||
"e51d501e2f994baba40345ad632eabee",
|
||
"631a85e420b64e8cb6915af59c5ce08a",
|
||
"70af9cb2838c4a92bd67f8cb5c98d97f",
|
||
"158115266c284c4f8dbce3586151cbf1",
|
||
"ce5019b36cde44c58c5f596dbb59a2f8",
|
||
"b90d660ca8584ba1815a3c66b420c079",
|
||
"7c4d1de626784a59a7e0a33c24086186",
|
||
"21cf0e35ecd845a8b5e7c5ce241cf177"
|
||
]
|
||
},
|
||
"collapsed": true,
|
||
"id": "DJkmoG2kq1_P",
|
||
"outputId": "8493ee59-c6ff-4bb6-d787-f295944db1cf"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Generating dev split: 100%|██████████| 5/5 [00:00<00:00, 139.81 examples/s]\n",
|
||
"Generating validation split: 100%|██████████| 30/30 [00:00<00:00, 258.29 examples/s]\n",
|
||
"Generating test split: 100%|██████████| 287/287 [00:01<00:00, 197.69 examples/s]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import datasets\n",
|
||
"\n",
|
||
"ds = datasets.load_dataset(path=name, name=subset, split=split)\n",
|
||
"ds = ds.select_columns([\"chat_completion_input\", \"input_query\", \"expected_answer\"])\n",
|
||
"eval_rows = ds.to_pandas().to_dict(orient=\"records\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "sqBA5LbNq7Xm"
|
||
},
|
||
"source": [
|
||
"- **Run Evaluation on Model Candidate**\n",
|
||
" - Define a System Prompt\n",
|
||
" - Define an EvalCandidate\n",
|
||
" - Run evaluate on datasets"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 441
|
||
},
|
||
"collapsed": true,
|
||
"id": "1r6qYTp9q5l7",
|
||
"outputId": "f1607a9b-c3a3-43cc-928f-0487d0438748"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100%|██████████| 5/5 [00:42<00:00, 8.60s/it]\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"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><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Answer: D'</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 image shows a sunflower leaf with small, dark spots and white powdery patches. The dark spots are likely caused by a fungal pathogen, such as rust or septoria leaf spot, while the white powdery patches are likely caused by a fungal pathogen, such as powdery mildew.\\n\\nSince there are two distinct types of lesions on the leaf, it is likely that there are two different pathogens infecting the leaf.\\n\\n**Answer:** B) Two pathogens'</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 question requires the identification of the reason behind the massive gum production on the trunks of grapefruit trees in Cyprus, despite appearing healthy from a distance. The correct answer can be deduced by analyzing the symptoms and considering the possible causes.\\n\\nTo determine the correct answer, let's evaluate each option:\\n\\nA) Don't know or not sure: This option is incorrect because it does not provide a specific reason for the gum production.\\n\\nB) Physiological stress: This option is also incorrect because it is too broad and does not specifically explain the gum production.\\n\\nC) Bacterial disease: This option is incorrect because bacterial diseases typically cause different symptoms such as leaf spots, blights, or wilting.\\n\\nD) Harvesting damage when cutting with knives: This option is incorrect because harvesting damage would likely cause wounds or scars on the tree, but it would not lead to massive gum production.\\n\\nE) Fungal gummosis: This option is the most likely cause of the gum production. Fungal gummosis is a common disease in citrus trees, including grapefruit, that causes the production of gum or sap on the trunks and branches. The disease is typically caused by fungi such as Phytophthora or Diplodia, which infect the tree through wounds or natural openings. The gum production is a defense mechanism by the tree to try to seal off the infection and prevent further damage.\\n\\nTherefore, the correct answer is:\\n\\nAnswer: E\"</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><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Answer: D'</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\">'**Causes of Splitting Petioles in Rhubarb**\\n\\nThe following factors can cause the petioles of rhubarb to split:\\n\\n* **Physiological Problems**: Issues such as water stress, nutrient deficiencies, or extreme temperatures can lead to splitting.\\n* **Phytoplasma Infection**: A bacterial infection caused by phytoplasma can lead to splitting of the petioles.\\n* **Animal Damage**: Pests like slugs, snails, or rodents can damage the plant and cause splitting.\\n* **Bacterial Infection**: Bacterial infections can also cause splitting.\\n\\nAs a result, the correct answer is:\\n\\n*Answer*: A) Physiological problems'</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\">'basic::regex_parser_multiple_choice_answer'</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\">'accuracy'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'accuracy'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.2</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'num_correct'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'num_total'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</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><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">}</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\">0.0</span><span style=\"font-weight: bold\">}</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\">0.0</span><span style=\"font-weight: bold\">}</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\">1.0</span><span style=\"font-weight: bold\">}</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\">0.0</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"
|
||
],
|
||
"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\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Answer: D'\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 image shows a sunflower leaf with small, dark spots and white powdery patches. The dark spots are likely caused by a fungal pathogen, such as rust or septoria leaf spot, while the white powdery patches are likely caused by a fungal pathogen, such as powdery mildew.\\n\\nSince there are two distinct types of lesions on the leaf, it is likely that there are two different pathogens infecting the leaf.\\n\\n**Answer:** B\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Two pathogens'\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 question requires the identification of the reason behind the massive gum production on the trunks of grapefruit trees in Cyprus, despite appearing healthy from a distance. The correct answer can be deduced by analyzing the symptoms and considering the possible causes.\\n\\nTo determine the correct answer, let's evaluate each option:\\n\\nA\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Don't know or not sure: This option is incorrect because it does not provide a specific reason for the gum production.\\n\\nB\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Physiological stress: This option is also incorrect because it is too broad and does not specifically explain the gum production.\\n\\nC\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Bacterial disease: This option is incorrect because bacterial diseases typically cause different symptoms such as leaf spots, blights, or wilting.\\n\\nD\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Harvesting damage when cutting with knives: This option is incorrect because harvesting damage would likely cause wounds or scars on the tree, but it would not lead to massive gum production.\\n\\nE\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Fungal gummosis: This option is the most likely cause of the gum production. Fungal gummosis is a common disease in citrus trees, including grapefruit, that causes the production of gum or sap on the trunks and branches. The disease is typically caused by fungi such as Phytophthora or Diplodia, which infect the tree through wounds or natural openings. The gum production is a defense mechanism by the tree to try to seal off the infection and prevent further damage.\\n\\nTherefore, the correct answer is:\\n\\nAnswer: E\"\u001b[0m\n",
|
||
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m,\n",
|
||
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Answer: D'\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'**Causes of Splitting Petioles in Rhubarb**\\n\\nThe following factors can cause the petioles of rhubarb to split:\\n\\n* **Physiological Problems**: Issues such as water stress, nutrient deficiencies, or extreme temperatures can lead to splitting.\\n* **Phytoplasma Infection**: A bacterial infection caused by phytoplasma can lead to splitting of the petioles.\\n* **Animal Damage**: Pests like slugs, snails, or rodents can damage the plant and cause splitting.\\n* **Bacterial Infection**: Bacterial infections can also cause splitting.\\n\\nAs a result, the correct answer is:\\n\\n*Answer*: A\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Physiological problems'\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'basic::regex_parser_multiple_choice_answer'\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'accuracy'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'accuracy'\u001b[0m: \u001b[1;36m0.2\u001b[0m, \u001b[32m'num_correct'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'num_total'\u001b[0m: \u001b[1;36m5\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\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.0\u001b[0m\u001b[1m}\u001b[0m, \u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.0\u001b[0m\u001b[1m}\u001b[0m, \u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.0\u001b[0m\u001b[1m}\u001b[0m, \u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m1.0\u001b[0m\u001b[1m}\u001b[0m, \u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.0\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[1m)\u001b[0m\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from rich.pretty import pprint\n",
|
||
"from tqdm import tqdm\n",
|
||
"\n",
|
||
"SYSTEM_PROMPT_TEMPLATE = \"\"\"\n",
|
||
"You are an expert in {subject} whose job is to answer questions from the user using images.\n",
|
||
"\n",
|
||
"First, reason about the correct answer.\n",
|
||
"\n",
|
||
"Then write the answer in the following format where X is exactly one of A,B,C,D:\n",
|
||
"\n",
|
||
"Answer: X\n",
|
||
"\n",
|
||
"Make sure X is one of A,B,C,D.\n",
|
||
"\n",
|
||
"If you are uncertain of the correct answer, guess the most likely one.\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"system_message = {\n",
|
||
" \"role\": \"system\",\n",
|
||
" \"content\": SYSTEM_PROMPT_TEMPLATE.format(subject=subset),\n",
|
||
"}\n",
|
||
"\n",
|
||
"client.benchmarks.register(\n",
|
||
" benchmark_id=\"meta-reference::mmmu\",\n",
|
||
" dataset_id=f\"mmmu-{subset}-{split}\",\n",
|
||
" scoring_functions=[\"basic::regex_parser_multiple_choice_answer\"],\n",
|
||
")\n",
|
||
"\n",
|
||
"response = client.eval.evaluate_rows_alpha(\n",
|
||
" benchmark_id=\"meta-reference::mmmu\",\n",
|
||
" input_rows=eval_rows,\n",
|
||
" scoring_functions=[\"basic::regex_parser_multiple_choice_answer\"],\n",
|
||
" task_config={\n",
|
||
" \"type\": \"benchmark\",\n",
|
||
" \"eval_candidate\": {\n",
|
||
" \"type\": \"model\",\n",
|
||
" \"model\": \"meta-llama/Llama-3.2-90B-Vision-Instruct\",\n",
|
||
" \"sampling_params\": {\n",
|
||
" \"strategy\": {\n",
|
||
" \"type\": \"top_p\",\n",
|
||
" \"temperature\": 1.0,\n",
|
||
" \"top_p\": 0.95,\n",
|
||
" },\n",
|
||
" \"max_tokens\": 4096,\n",
|
||
" \"repeat_penalty\": 1.0,\n",
|
||
" },\n",
|
||
" \"system_message\": system_message,\n",
|
||
" },\n",
|
||
" },\n",
|
||
")\n",
|
||
"pprint(response)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "vYlb9wKzwg-s"
|
||
},
|
||
"source": [
|
||
"#### 1.2. Running SimpleQA\n",
|
||
"- We will use a pre-processed SimpleQA dataset from [llamastack/evals](https://huggingface.co/datasets/llamastack/evals/viewer/evals__simpleqa) which is obtained by transforming the input query into correct format accepted by `inference/chat-completion` API.\n",
|
||
"- Since we will be using this same dataset in our next example for Agentic evaluation, we will register it using the `/datasets` API, and interact with it through `/datasetio` API."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {
|
||
"id": "HXmZf3Ymw-aX"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"simpleqa_dataset_id = \"huggingface::simpleqa\"\n",
|
||
"\n",
|
||
"_ = client.datasets.register(\n",
|
||
" dataset_id=simpleqa_dataset_id,\n",
|
||
" provider_id=\"huggingface\",\n",
|
||
" url={\"uri\": \"https://huggingface.co/datasets/llamastack/evals\"},\n",
|
||
" metadata={\n",
|
||
" \"path\": \"llamastack/evals\",\n",
|
||
" \"name\": \"evals__simpleqa\",\n",
|
||
" \"split\": \"train\",\n",
|
||
" },\n",
|
||
" dataset_schema={\n",
|
||
" \"input_query\": {\"type\": \"string\"},\n",
|
||
" \"expected_answer\": {\"type\": \"string\"},\n",
|
||
" \"chat_completion_input\": {\"type\": \"chat_completion_input\"},\n",
|
||
" },\n",
|
||
")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {
|
||
"id": "Gc8azb4Rxr5J"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"eval_rows = client.datasetio.get_rows_paginated(\n",
|
||
" dataset_id=simpleqa_dataset_id,\n",
|
||
" rows_in_page=5,\n",
|
||
")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
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|
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},
|
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"outputs": [
|
||
{
|
||
"name": "stderr",
|
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"output_type": "stream",
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||
"text": [
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" 0%| | 0/5 [00:00<?, ?it/s]"
|
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]
|
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},
|
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{
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"name": "stderr",
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"output_type": "stream",
|
||
"text": [
|
||
"100%|██████████| 5/5 [00:31<00:00, 6.38s/it]\n"
|
||
]
|
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|
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{
|
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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",
|
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"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"I'm not sure who received the IEEE Frank Rosenblatt Award in 2010.\"</span><span style=\"font-weight: bold\">}</span>,\n",
|
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"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"I'm not aware of the information about the 2018 Jerlov Award recipient.\"</span><span style=\"font-weight: bold\">}</span>,\n",
|
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|
||
"<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\">\"Radcliffe College was a women's liberal arts college in Cambridge, Massachusetts. However, it merged with Harvard University in 1977 and is now known as the Radcliffe Institute for Advanced Study at Harvard University.\"</span>\n",
|
||
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>,\n",
|
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|
||
"<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\">\"I am unable to verify what Empress Elizabeth of Austria's favorite sculpture depicted at her villa Achilleion at Corfu, according to Karl Küchler.\"</span>\n",
|
||
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>\n",
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|
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|
||
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'llm-as-judge::405b-simpleqa'</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>,\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: #008000; text-decoration-color: #008000\">'C'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'C'</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: #008000; text-decoration-color: #008000\">'C'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'C'</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: #008000; text-decoration-color: #008000\">'A'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'A'</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: #008000; text-decoration-color: #008000\">'C'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'C'</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: #008000; text-decoration-color: #008000\">'C'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'C'</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"
|
||
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|
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|
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|
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|
||
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"I'm not sure who received the IEEE Frank Rosenblatt Award in 2010.\"\u001b[0m\u001b[1m}\u001b[0m,\n",
|
||
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"I'm not aware of the information about the 2018 Jerlov Award recipient.\"\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\"Radcliffe College was a women's liberal arts college in Cambridge, Massachusetts. However, it merged with Harvard University in 1977 and is now known as the Radcliffe Institute for Advanced Study at Harvard University.\"\u001b[0m\n",
|
||
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m,\n",
|
||
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'I do not have information on the Leipzig 1877 tournament.'\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\"I am unable to verify what Empress Elizabeth of Austria's favorite sculpture depicted at her villa Achilleion at Corfu, according to Karl Küchler.\"\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'llm-as-judge::405b-simpleqa'\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[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[32m'C'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'C'\u001b[0m\u001b[1m}\u001b[0m,\n",
|
||
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'C'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'C'\u001b[0m\u001b[1m}\u001b[0m,\n",
|
||
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'A'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'A'\u001b[0m\u001b[1m}\u001b[0m,\n",
|
||
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'C'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'C'\u001b[0m\u001b[1m}\u001b[0m,\n",
|
||
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'C'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'C'\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"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# register 405B as LLM Judge model\n",
|
||
"client.models.register(\n",
|
||
" model_id=\"meta-llama/Llama-3.1-405B-Instruct\",\n",
|
||
" provider_model_id=\"meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo\",\n",
|
||
" provider_id=\"together\",\n",
|
||
")\n",
|
||
"\n",
|
||
"client.benchmarks.register(\n",
|
||
" benchmark_id=\"meta-reference::simpleqa\",\n",
|
||
" dataset_id=simpleqa_dataset_id,\n",
|
||
" scoring_functions=[\"llm-as-judge::405b-simpleqa\"],\n",
|
||
")\n",
|
||
"\n",
|
||
"response = client.eval.evaluate_rows_alpha(\n",
|
||
" benchmark_id=\"meta-reference::simpleqa\",\n",
|
||
" input_rows=eval_rows.rows,\n",
|
||
" scoring_functions=[\"llm-as-judge::405b-simpleqa\"],\n",
|
||
" task_config={\n",
|
||
" \"type\": \"benchmark\",\n",
|
||
" \"eval_candidate\": {\n",
|
||
" \"type\": \"model\",\n",
|
||
" \"model\": \"meta-llama/Llama-3.2-90B-Vision-Instruct\",\n",
|
||
" \"sampling_params\": {\n",
|
||
" \"strategy\": {\n",
|
||
" \"type\": \"greedy\",\n",
|
||
" },\n",
|
||
" \"max_tokens\": 4096,\n",
|
||
" \"repeat_penalty\": 1.0,\n",
|
||
" },\n",
|
||
" },\n",
|
||
" },\n",
|
||
")\n",
|
||
"pprint(response)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "eyziqe_Em6d6"
|
||
},
|
||
"source": [
|
||
"## 2. Agentic Evaluation\n",
|
||
"\n",
|
||
"- In this example, we will demonstrate how to evaluate a agent candidate served by Llama Stack via `/agent` API.\n",
|
||
"\n",
|
||
"- We will continue to use the SimpleQA dataset we used in previous example.\n",
|
||
"\n",
|
||
"- Instead of running evaluation on model, we will run the evaluation on a Search Agent with access to search tool. We will define our agent evaluation candidate through `AgentConfig`.\n",
|
||
"\n",
|
||
"> You will need to set the `TAVILY_SEARCH_API_KEY` in Secrets of this notebook."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
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"height": 538
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},
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"id": "mxLCsP4MvFqP",
|
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"outputId": "8be2a32f-2a47-4443-8992-0000c23ca678"
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},
|
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"outputs": [
|
||
{
|
||
"name": "stderr",
|
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"output_type": "stream",
|
||
"text": [
|
||
"5it [00:06, 1.33s/it]\n"
|
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]
|
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},
|
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{
|
||
"data": {
|
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|
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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 IEEE Frank Rosenblatt Award was given to Professor John Shawe-Taylor in 2010 for his contributions to the foundations of kernel methods.'</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 Jerlov Award is given by The Oceanography Society to recognize outstanding contributions to the field of ocean optics. The 2018 Jerlov Award was awarded to Dr. Kendall L. Carder.'</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 women's liberal arts college in Cambridge, Massachusetts is Radcliffe College. However, in 1999, Radcliffe College merged with Harvard University to form the Radcliffe Institute for Advanced Study at Harvard University. The institute is still located in Cambridge, Massachusetts, and is dedicated to supporting women's education and research.\"</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><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'The Leipzig 1877 tournament was organized in honor of Adolf Anderssen.'</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\">\"According to Karl Küchler, Empress Elizabeth of Austria's favorite sculpture, which was made for her villa Achilleion at Corfu, depicted the Dying Achilles.\"</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\">'llm-as-judge::405b-simpleqa'</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>,\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: #008000; text-decoration-color: #008000\">'B'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'B'</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: #008000; text-decoration-color: #008000\">'B'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'B'</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: #008000; text-decoration-color: #008000\">'A'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'A'</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: #008000; text-decoration-color: #008000\">'A'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'A'</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: #008000; text-decoration-color: #008000\">'B'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'B'</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"
|
||
],
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|
||
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"According to Karl Küchler, Empress Elizabeth of Austria's favorite sculpture, which was made for her villa Achilleion at Corfu, depicted the Dying Achilles.\"\u001b[0m\n",
|
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"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'B'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'B'\u001b[0m\u001b[1m}\u001b[0m,\n",
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"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'A'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'A'\u001b[0m\u001b[1m}\u001b[0m,\n",
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}
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"agent_config = {\n",
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" \"instructions\": \"You are a helpful assistant that have access to tool to search the web. \",\n",
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