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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4",
"machine_shape": "hm"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"source": [
"Initial setup"
],
"metadata": {
"id": "U8RTc2PmnX-v"
}
},
{
"cell_type": "code",
"source": [
"!pip install -r https://huggingface.co/flunardelli/llm-metaeval/raw/main/requirements.txt"
],
"metadata": {
"id": "kGW7vfRkrqHe"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"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='pub'\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)"
],
"metadata": {
"id": "IHxFvAC4eSnW"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Create task for PUB all datasets"
],
"metadata": {
"id": "Jd2JwKZaPkNS"
}
},
{
"cell_type": "code",
"source": [
"YAML_template_pub_tasks = [\n",
" (\"task_1\", 2),\n",
" (\"task_2\", 5),\n",
" (\"task_3\", 5),\n",
" (\"task_4\", 3),\n",
" (\"task_5\", 2),\n",
" (\"task_6\", 2),\n",
" (\"task_7\", 2),\n",
" (\"task_8\", 2),\n",
" (\"task_9\", 2),\n",
" (\"task_10\", 3),\n",
" (\"task_11\", 3),\n",
" (\"task_12\", 2),\n",
" (\"task_13\", 2),\n",
" (\"task_14\", 4)\n",
"]\n",
"\n",
"default_doc_to_text = \"{{pretext.strip()}}\\n {{options[0]}}\\n{{options[1]}}\\\\n{{options[2]}}\\\\n{{options[3]}}\\\\n{{options[4]}}\\\\nAnswer:\"\n",
"\n",
"\n",
"YAML_template_pub_base = \"\"\"\n",
"task: __task_name__\n",
"dataset_path: flunardelli/PUB\n",
"dataset_name: __dataset_name__\n",
"description: \"PUB\"\n",
"test_split: test\n",
"fewshot_split: test\n",
"fewshot_config:\n",
" sampler: first_n\n",
"num_fewshot: 5\n",
"output_type: multiple_choice\n",
"doc_to_text: \"{{pretext.strip()}}\\n Options:\\n__options__\\nAnswer:\"\n",
"doc_to_choice: \"{{options}}\"\n",
"doc_to_target: \"correct answer\"\n",
"metric_list:\n",
" - metric: acc\n",
" aggregation: mean\n",
" higher_is_better: true\n",
"\"\"\"\n",
"tasks = []\n",
"for t in YAML_template_pub_tasks:\n",
" dataset_name, num_choices = t\n",
" task_name = f\"pub_{dataset_name}\"\n",
" tasks.append(task_name)\n",
" templace_choices = '\\n'.join([\"{{options[__i__]}}\".replace('__i__',str(i)) for i in range(num_choices)])\n",
" template = (YAML_template_pub_base\n",
" .replace('__options__',templace_choices)\n",
" .replace('__dataset_name__',dataset_name).replace('__task_name__',task_name)\n",
" )\n",
" create_task(template, f\"pub_{dataset_name}.yaml\")\n",
"\n",
"os.environ['TASKS'] = ','.join(tasks)"
],
"metadata": {
"id": "xP0cC_sHih7C"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Mistral Models"
],
"metadata": {
"id": "1fEX-49hQ-Be"
}
},
{
"cell_type": "code",
"source": [
"!for i in $(echo $TASKS|tr ',' ' '); do lm_eval \\\n",
"--model hf --model_args pretrained=mistralai/Mixtral-8x7B-Instruct-v0.1,revision=41bd4c9e7e4fb318ca40e721131d4933966c2cc1,trust_remote_code=False,dtype=bfloat16,parallelize=True \\\n",
"--tasks $i \\\n",
"--include_path $TASK_FOLDER/. --output $OUTPUT_FOLDER --log_samples \\\n",
"--batch_size auto; done &> run.log"
],
"metadata": {
"id": "v_MTb0t1XHOJ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"hf_upload_folder(BASE_FOLDER)"
],
"metadata": {
"id": "vUf27nLYXGzh"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!for i in $(echo $TASKS|tr ',' ' '); do lm_eval \\\n",
"--model hf --model_args pretrained=mistralai/Mixtral-8x22B-v0.1,revision=b03e260818710044a2f088d88fab12bb220884fb,trust_remote_code=False,dtype=bfloat16,parallelize=True \\\n",
"--tasks $i \\\n",
"--include_path $TASK_FOLDER/. --output $OUTPUT_FOLDER --log_samples \\\n",
"--batch_size auto; done &> run.log"
],
"metadata": {
"id": "LPqTo2z29RKx"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"hf_upload_folder(BASE_FOLDER)"
],
"metadata": {
"id": "ZQl05b1rf83u"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "ZUTPHnV0kMB1"
}
}
]
} |