{
  "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('HUGGING_FACE_WRITE_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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJjo_A5tP-Td"
      },
      "source": [
        "Llama Models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "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 mmlu_all \\\n",
        "--include_path $TASK_FOLDER/. --output $OUTPUT_FOLDER --use_cache cache --log_samples \\\n",
        "--batch_size 16\n",
        "#--limit 10 \\"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "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 mmlu_all \\\n",
        "--include_path $TASK_FOLDER/. --output $OUTPUT_FOLDER --use_cache cache --log_samples \\\n",
        "--batch_size 16\n",
        "#--limit 10 \\"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "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 mmlu_all \\\n",
        "--include_path $TASK_FOLDER/. --output $OUTPUT_FOLDER --use_cache cache --log_samples \\\n",
        "--batch_size 16\n",
        "#--limit 10 \\"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1fEX-49hQ-Be"
      },
      "source": [
        "Mistral Models"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!accelerate launch -m lm_eval \\\n",
        "--model hf --model_args pretrained=mistralai/Mixtral-8x7B-Instruct-v0.1,parallelize=True \\\n",
        "--tasks mmlu_all \\\n",
        "--include_path $TASK_FOLDER/. --output $OUTPUT_FOLDER --use_cache cache --log_samples \\\n",
        "--batch_size 16\n",
        "#--limit 10 \\"
      ],
      "metadata": {
        "id": "ilu9_ulWTy3p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3cHI2qxN2fJ0"
      },
      "outputs": [],
      "source": [
        "!accelerate launch -m lm_eval \\\n",
        "--model hf --model_args pretrained=mistralai/Mixtral-8x22B-v0.1,parallelize=True \\\n",
        "--tasks mmlu_all \\\n",
        "--include_path $TASK_FOLDER/. --output $OUTPUT_FOLDER --use_cache cache --log_samples \\\n",
        "--batch_size 16\n",
        "#--limit 10 \\"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZUTPHnV0kMB1"
      },
      "source": [
        "Save output results"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hf_upload_folder(BASE_FOLDER)"
      ],
      "metadata": {
        "id": "mGGdqBNBzFYL"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}