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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "U8RTc2PmnX-v"
},
"source": [
"Initial setup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kGW7vfRkrqHe"
},
"outputs": [],
"source": [
"!pip install -r https://huggingface.co/flunardelli/llm-metaeval/raw/main/requirements.txt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2I850FIsCVNw"
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"import os\n",
"from huggingface_hub import login, upload_folder\n",
"from google.colab import userdata\n",
"import shutil\n",
"\n",
"HF_TOKEN = userdata.get('HF_TOKEN')\n",
"login(HF_TOKEN, True)\n",
"BASE_DATASET='mmlu'\n",
"REPO_ID='flunardelli/llm-metaeval'\n",
"BASE_FOLDER=f\"/content/{BASE_DATASET}/\"#{datetime.now().strftime('%Y-%m-%dT%H-%M-%S')}\n",
"OUTPUT_FOLDER=os.path.join(BASE_FOLDER,'output')\n",
"TASK_FOLDER=os.path.join(BASE_FOLDER,'tasks')\n",
"#shutil.rmtree(BASE_FOLDER)\n",
"os.makedirs(OUTPUT_FOLDER)\n",
"os.makedirs(TASK_FOLDER)\n",
"os.environ['HF_TOKEN'] = HF_TOKEN\n",
"os.environ['OUTPUT_FOLDER'] = OUTPUT_FOLDER\n",
"os.environ['TASK_FOLDER'] = TASK_FOLDER\n",
"\n",
"def hf_upload_folder(folder_path):\n",
" upload_folder(\n",
" folder_path=folder_path,\n",
" path_in_repo=\"evals/\",\n",
" repo_id=REPO_ID,\n",
" token=HF_TOKEN,\n",
" repo_type=\"dataset\"\n",
" )\n",
"\n",
"def create_task(content, filename):\n",
" filename_path = os.path.join(TASK_FOLDER,filename)\n",
" with open(filename_path, \"w\") as f:\n",
" f.write(content)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Jd2JwKZaPkNS"
},
"source": [
"Create task for MMLU all datasets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xP0cC_sHih7C"
},
"outputs": [],
"source": [
"YAML_mmlu_en_us_string = \"\"\"\n",
"task: mmlu_all\n",
"dataset_path: cais/mmlu\n",
"dataset_name: all\n",
"description: \"MMLU dataset\"\n",
"test_split: test\n",
"fewshot_split: dev\n",
"fewshot_config:\n",
" sampler: first_n\n",
"output_type: multiple_choice\n",
"doc_to_text: \"{{question.strip()}}\\nA. {{choices[0]}}\\nB. {{choices[1]}}\\nC. {{choices[2]}}\\nD. {{choices[3]}}\\nAnswer:\"\n",
"doc_to_choice: [\"A\", \"B\", \"C\", \"D\"]\n",
"doc_to_target: answer\n",
"metric_list:\n",
" - metric: acc\n",
" aggregation: mean\n",
" higher_is_better: true\n",
" - metric: acc_norm\n",
" aggregation: mean\n",
" higher_is_better: true\n",
"\"\"\"\n",
"create_task(YAML_mmlu_en_us_string, 'mmlu_en_us.yaml')\n",
"os.environ['TASKS'] = 'mmlu_all'\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mJjo_A5tP-Td"
},
"source": [
"Llama Models"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "IzP5nyP0Gwk8"
},
"outputs": [],
"source": [
"!accelerate launch -m lm_eval \\\n",
"--model hf --model_args pretrained=meta-llama/Llama-3.2-1B-Instruct,parallelize=True \\\n",
"--tasks $TASKS \\\n",
"--include_path $TASK_FOLDER/. --output $OUTPUT_FOLDER --use_cache cache --log_samples \\\n",
"--batch_size 16\n",
"#--limit 10 \\"
]
},
{
"cell_type": "code",
"source": [
"hf_upload_folder(BASE_FOLDER)"
],
"metadata": {
"id": "uMoitxJkHerH"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "oIACOAhDW5ow"
},
"outputs": [],
"source": [
"!accelerate launch -m lm_eval \\\n",
"--model hf --model_args pretrained=meta-llama/Llama-3.2-3B-Instruct,parallelize=True \\\n",
"--tasks $TASKS \\\n",
"--include_path $TASK_FOLDER/. --output $OUTPUT_FOLDER --use_cache cache --log_samples \\\n",
"--batch_size 16\n",
"#--limit 10 \\"
]
},
{
"cell_type": "code",
"source": [
"hf_upload_folder(BASE_FOLDER)"
],
"metadata": {
"id": "eIUOqu5sHfkM"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "cFFYPzBIYGf7"
},
"outputs": [],
"source": [
"!accelerate launch -m lm_eval \\\n",
"--model hf --model_args pretrained=meta-llama/Meta-Llama-3-8B,parallelize=True \\\n",
"--tasks $TASKS \\\n",
"--include_path $TASK_FOLDER/. --output $OUTPUT_FOLDER --use_cache cache --log_samples \\\n",
"--batch_size 16\n",
"#--limit 10 \\"
]
},
{
"cell_type": "code",
"source": [
"hf_upload_folder(BASE_FOLDER)"
],
"metadata": {
"id": "xsL82Q4SHgMn"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "1fEX-49hQ-Be"
},
"source": [
"Mistral Models"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "ilu9_ulWTy3p"
},
"outputs": [],
"source": [
"!accelerate launch -m lm_eval \\\n",
"--model hf --model_args pretrained=mistralai/Mixtral-8x7B-Instruct-v0.1,parallelize=True \\\n",
"--tasks $TASKS \\\n",
"--include_path $TASK_FOLDER/. --output $OUTPUT_FOLDER --use_cache cache --log_samples \\\n",
"--batch_size 16\n",
"#--limit 10 \\"
]
},
{
"cell_type": "code",
"source": [
"hf_upload_folder(BASE_FOLDER)"
],
"metadata": {
"id": "jE5r8gVDHhAz"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3cHI2qxN2fJ0"
},
"outputs": [],
"source": [
"!accelerate launch --multi_gpu --num_processes 4 -m lm_eval \\\n",
"--model hf --model_args pretrained=mistralai/Mixtral-8x22B-v0.1 \\\n",
"--tasks $TASKS \\\n",
"--include_path $TASK_FOLDER/. --output $OUTPUT_FOLDER --use_cache cache --log_samples \\\n",
"--batch_size 8\n",
"#--limit 10 \\"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mGGdqBNBzFYL"
},
"outputs": [],
"source": [
"hf_upload_folder(BASE_FOLDER)"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "L4",
"machine_shape": "hm",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |