Fine_Tuning_Mistral7B.ipynb

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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/SolomonAmaning/Anaemia-Prediction/blob/main/Fine_Tuning_Mistral7B.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "source": [
        "\n",
        "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES\n",
        "# TO THE CORRECT LOCATION (/kaggle/input) IN YOUR NOTEBOOK,\n",
        "# THEN FEEL FREE TO DELETE THIS CELL.\n",
        "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
        "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
        "# NOTEBOOK.\n",
        "\n",
        "import os\n",
        "import sys\n",
        "from tempfile import NamedTemporaryFile\n",
        "from urllib.request import urlopen\n",
        "from urllib.parse import unquote, urlparse\n",
        "from urllib.error import HTTPError\n",
        "from zipfile import ZipFile\n",
        "import tarfile\n",
        "import shutil\n",
        "\n",
        "CHUNK_SIZE = 40960\n",
        "DATA_SOURCE_MAPPING = 'mts-dataset-clinical-dialogue-and-summary:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F5159321%2F8619417%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240607%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240607T074307Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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,mistral/pytorch/7b-instruct-v0.1-hf/1:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-models-data%2F3900%2F5112%2Fbundle%2Farchive.tar.gz%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240607%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240607T074307Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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'\n",
        "\n",
        "KAGGLE_INPUT_PATH='/kaggle/input'\n",
        "KAGGLE_WORKING_PATH='/kaggle/working'\n",
        "KAGGLE_SYMLINK='kaggle'\n",
        "\n",
        "!umount /kaggle/input/ 2> /dev/null\n",
        "shutil.rmtree('/kaggle/input', ignore_errors=True)\n",
        "os.makedirs(KAGGLE_INPUT_PATH, 0o777, exist_ok=True)\n",
        "os.makedirs(KAGGLE_WORKING_PATH, 0o777, exist_ok=True)\n",
        "\n",
        "try:\n",
        "  os.symlink(KAGGLE_INPUT_PATH, os.path.join(\"..\", 'input'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "try:\n",
        "  os.symlink(KAGGLE_WORKING_PATH, os.path.join(\"..\", 'working'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "\n",
        "for data_source_mapping in DATA_SOURCE_MAPPING.split(','):\n",
        "    directory, download_url_encoded = data_source_mapping.split(':')\n",
        "    download_url = unquote(download_url_encoded)\n",
        "    filename = urlparse(download_url).path\n",
        "    destination_path = os.path.join(KAGGLE_INPUT_PATH, directory)\n",
        "    try:\n",
        "        with urlopen(download_url) as fileres, NamedTemporaryFile() as tfile:\n",
        "            total_length = fileres.headers['content-length']\n",
        "            print(f'Downloading {directory}, {total_length} bytes compressed')\n",
        "            dl = 0\n",
        "            data = fileres.read(CHUNK_SIZE)\n",
        "            while len(data) > 0:\n",
        "                dl += len(data)\n",
        "                tfile.write(data)\n",
        "                done = int(50 * dl / int(total_length))\n",
        "                sys.stdout.write(f\"\\r[{'=' * done}{' ' * (50-done)}] {dl} bytes downloaded\")\n",
        "                sys.stdout.flush()\n",
        "                data = fileres.read(CHUNK_SIZE)\n",
        "            if filename.endswith('.zip'):\n",
        "              with ZipFile(tfile) as zfile:\n",
        "                zfile.extractall(destination_path)\n",
        "            else:\n",
        "              with tarfile.open(tfile.name) as tarfile:\n",
        "                tarfile.extractall(destination_path)\n",
        "            print(f'\\nDownloaded and uncompressed: {directory}')\n",
        "    except HTTPError as e:\n",
        "        print(f'Failed to load (likely expired) {download_url} to path {destination_path}')\n",
        "        continue\n",
        "    except OSError as e:\n",
        "        print(f'Failed to load {download_url} to path {destination_path}')\n",
        "        continue\n",
        "\n",
        "print('Data source import complete.')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ReTk1fzpuzs",
        "outputId": "cfb8a024-0984-4f4c-cf3a-3c1ef76dbd00"
      },
      "cell_type": "code",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading mts-dataset-clinical-dialogue-and-summary, 413600 bytes compressed\n",
            "[==================================================] 413600 bytes downloaded\n",
            "Downloaded and uncompressed: mts-dataset-clinical-dialogue-and-summary\n",
            "Downloading mistral/pytorch/7b-instruct-v0.1-hf/1, 11936623935 bytes compressed\n",
            "[==================================================] 11936623935 bytes downloaded\n",
            "Downloaded and uncompressed: mistral/pytorch/7b-instruct-v0.1-hf/1\n",
            "Data source import complete.\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_oXXe0Eipuzt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n",
        "\n",
        "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\"\n",
        "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
      ],
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "execution": {
          "iopub.status.busy": "2024-06-07T04:07:21.06154Z",
          "iopub.execute_input": "2024-06-07T04:07:21.062206Z",
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          "shell.execute_reply": "2024-06-07T04:07:21.422707Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJ8Rtsiepuzt",
        "outputId": "1a589bb7-93fe-4045-b022-79641a381243"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/kaggle/input/mistral/pytorch/7b-instruct-v0.1-hf/1/pytorch_model.bin.index.json\n",
            "/kaggle/input/mistral/pytorch/7b-instruct-v0.1-hf/1/tokenizer.json\n",
            "/kaggle/input/mistral/pytorch/7b-instruct-v0.1-hf/1/special_tokens_map.json\n",
            "/kaggle/input/mistral/pytorch/7b-instruct-v0.1-hf/1/config.json\n",
            "/kaggle/input/mistral/pytorch/7b-instruct-v0.1-hf/1/tokenizer_config.json\n",
            "/kaggle/input/mistral/pytorch/7b-instruct-v0.1-hf/1/pytorch_model-00002-of-00002.bin\n",
            "/kaggle/input/mistral/pytorch/7b-instruct-v0.1-hf/1/generation_config.json\n",
            "/kaggle/input/mistral/pytorch/7b-instruct-v0.1-hf/1/tokenizer.model\n",
            "/kaggle/input/mistral/pytorch/7b-instruct-v0.1-hf/1/pytorch_model-00001-of-00002.bin\n",
            "/kaggle/input/mistral/pytorch/7b-instruct-v0.1-hf/1/.gitattributes\n",
            "/kaggle/input/mts-dataset-clinical-dialogue-and-summary/MTS-Dialog-TestSet-1-MEDIQA-Chat-2023.csv\n",
            "/kaggle/input/mts-dataset-clinical-dialogue-and-summary/MTS-Dialog-ValidationSet.csv\n",
            "/kaggle/input/mts-dataset-clinical-dialogue-and-summary/MTS-Dialog-TrainingSet.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "%pip install -U bitsandbytes\n",
        "%pip install -U transformers\n",
        "%pip install -U peft\n",
        "%pip install -U accelerate\n",
        "%pip install -U trl"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-07T04:07:26.755807Z",
          "iopub.execute_input": "2024-06-07T04:07:26.756279Z",
          "iopub.status.idle": "2024-06-07T04:08:33.570117Z",
          "shell.execute_reply.started": "2024-06-07T04:07:26.756248Z",
          "shell.execute_reply": "2024-06-07T04:08:33.568847Z"
        },
        "trusted": true,
        "id": "3ESP2beApuzu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig,HfArgumentParser,TrainingArguments,pipeline, logging"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-07T04:08:51.063764Z",
          "iopub.execute_input": "2024-06-07T04:08:51.064772Z",
          "iopub.status.idle": "2024-06-07T04:09:07.469625Z",
          "shell.execute_reply.started": "2024-06-07T04:08:51.064722Z",
          "shell.execute_reply": "2024-06-07T04:09:07.468842Z"
        },
        "trusted": true,
        "id": "cGg8oz_9puzu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-07T04:13:06.683797Z",
          "iopub.execute_input": "2024-06-07T04:13:06.684788Z",
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          "shell.execute_reply.started": "2024-06-07T04:13:06.684751Z",
          "shell.execute_reply": "2024-06-07T04:13:07.814398Z"
        },
        "trusted": true,
        "id": "yUCZ_OBwpuzu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "%pip install wandb\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-07T04:13:11.208794Z",
          "iopub.execute_input": "2024-06-07T04:13:11.209397Z",
          "iopub.status.idle": "2024-06-07T04:13:11.790204Z",
          "shell.execute_reply.started": "2024-06-07T04:13:11.209366Z",
          "shell.execute_reply": "2024-06-07T04:13:11.789447Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Q_HTYxapuzu",
        "outputId": "21dbaa54-0149-4cf2-dc07-46f607111ca1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting wandb\n",
            "  Downloading wandb-0.17.1-py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.8 MB)\n",
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            "  Downloading docker_pycreds-0.4.0-py2.py3-none-any.whl (9.0 kB)\n",
            "Collecting gitpython!=3.1.29,>=1.0.0 (from wandb)\n",
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            "\u001b[?25hCollecting setproctitle (from wandb)\n",
            "  Downloading setproctitle-1.3.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (30 kB)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2024.6.2)\n",
            "Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->gitpython!=3.1.29,>=1.0.0->wandb)\n",
            "  Downloading smmap-5.0.1-py3-none-any.whl (24 kB)\n",
            "Installing collected packages: smmap, setproctitle, sentry-sdk, docker-pycreds, gitdb, gitpython, wandb\n",
            "Successfully installed docker-pycreds-0.4.0 gitdb-4.0.11 gitpython-3.1.43 sentry-sdk-2.5.0 setproctitle-1.3.3 smmap-5.0.1 wandb-0.17.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import torch\n",
        "import wandb"
      ],
      "metadata": {
        "id": "xERQ2QR2xHoP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-07T04:13:14.273734Z",
          "iopub.execute_input": "2024-06-07T04:13:14.274529Z",
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          "shell.execute_reply": "2024-06-07T04:13:14.658257Z"
        },
        "trusted": true,
        "id": "lJl3_p4dpuzu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from trl import SFTTrainer"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-07T04:13:16.884761Z",
          "iopub.execute_input": "2024-06-07T04:13:16.885444Z",
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        },
        "trusted": true,
        "id": "-fjrgEUjpuzu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from kaggle_secrets import UserSecretsClient\n",
        "from google.colab import userdata\n",
        "userdata.get('hf')\n",
        "# user_secrets = UserSecretsClient()\n",
        "# secret_value_0 = user_secrets.get_secret(\"hf\")\n",
        "# secret_value_1 = user_secrets.get_secret(\"wandb\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-07T04:13:19.805939Z",
          "iopub.execute_input": "2024-06-07T04:13:19.80665Z",
          "iopub.status.idle": "2024-06-07T04:13:19.983531Z",
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          "shell.execute_reply": "2024-06-07T04:13:19.982612Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "2tZZKVDLpuzu",
        "outputId": "3c3aea4a-6222-462c-92af-8fa0b34d7703"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'01'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.environ['HUGGINGFACE_TOKEN'] = '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'\n",
        "hf_token = os.getenv('HUGGINGFACE_TOKEN')\n",
        "!huggingface-cli login --token $hf_token"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-07T04:13:23.787494Z",
          "iopub.execute_input": "2024-06-07T04:13:23.788114Z",
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          "shell.execute_reply.started": "2024-06-07T04:13:23.788081Z",
          "shell.execute_reply": "2024-06-07T04:13:25.301127Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dWxdvC8npuzu",
        "outputId": "52aeea93-79c3-4515-8f64-19dbc8b50d82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Set the W&B API key (replace 'your_wandb_api_key' with your actual API key)\n",
        "os.environ['WANDB_API_KEY'] = '5e1f28313d35321122498449072979d264eff823'\n",
        "\n",
        "# Login to W&B using the environment variable\n",
        "import wandb\n",
        "wandb.login()\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-07T04:13:30.13054Z",
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        "trusted": true,
        "colab": {
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        "outputId": "5a0fff09-3ba4-4896-ef33-fa574fce2e63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33msolomonodum\u001b[0m (\u001b[33msolomon-odum\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "# Initialize a W&B run\n",
        "run = wandb.init(\n",
        "    project='Fine tunig mistral 7b',\n",
        "    job_type='training',\n",
        "    anonymous='allow'\n",
        ")\n"
      ],
      "metadata": {
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        "trusted": true,
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        "outputId": "d65fad3a-86d3-4a12-e01b-daff0d8c1c6d"
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      "outputs": [
        {
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            ],
            "text/html": [
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            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20240607_082146-lxqbjfht</code>"
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            "text/html": [
              " View project at <a href='https://wandb.ai/solomon-odum/Fine%20tunig%20mistral%207b' target=\"_blank\">https://wandb.ai/solomon-odum/Fine%20tunig%20mistral%207b</a>"
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    {
      "cell_type": "code",
      "source": [
        "base_model = \"/kaggle/input/mistral/pytorch/7b-instruct-v0.1-hf/1\""
      ],
      "metadata": {
        "execution": {
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          "iopub.execute_input": "2024-06-07T04:37:41.19216Z",
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        "trusted": true,
        "id": "Pr2i_yG7puzu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = \"mistralai/Mistral-7B-Instruct-v0.1\"\n",
        "dataset_name = \"har1/MTS_Dialogue-Clinical_Note\"\n",
        "new_model = \"mistral_7b-instruct-new\""
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      "metadata": {
        "execution": {
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Importing the dataset\n",
        "dataset = load_dataset(dataset_name, split=\"train\")\n",
        "dataset[\"dialogue\"][100]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-06T10:33:31.637884Z",
          "iopub.execute_input": "2024-06-06T10:33:31.638356Z",
          "iopub.status.idle": "2024-06-06T10:33:32.24429Z",
          "shell.execute_reply.started": "2024-06-06T10:33:31.638324Z",
          "shell.execute_reply": "2024-06-06T10:33:32.242487Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362,
          "referenced_widgets": [
            "ad4757ac74644ef19b438faa08f50884",
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            "a72dc6f8b31a463b998ab99c66bd0f87",
            "924373ef8ccf41d697657076a07486c3",
            "79e81005106d49409dd2d2da4de306e9",
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            "0e0c9595ff9f4081a23b8292e15b5aab",
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            "ec0f0002877e45e88f5466ab91151b16",
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            "4d6dfbaa445f4993bc39ef812444a93a",
            "5700a672a20e48b0b8300317c0b862dd",
            "dbb3a10a53ba4c028d619abc1d5cf177",
            "e7bfd8da2a384c508c38b1394d3716fd",
            "afbb089f02644c74b0942b5624e623cb",
            "a166ac607be644e88575fb63c7dcc28a",
            "6e422e7fbea8461db41dd3b4da2aa26e",
            "44711684b41240f5b4de13c40a604931",
            "4822d77c014948549391d06eb5c83122"
          ]
        },
        "id": "r0Rgzfvjpuzv",
        "outputId": "a80f87dd-cf0d-498a-c09f-147540d5cb9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/1.67k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ad4757ac74644ef19b438faa08f50884"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.02M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b6a0e765affc43259b997c214610fe1f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/74.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3a9112208ff74da089565168c4fd1732"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/1301 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92f86cafc289412db5936a6f29340bbf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Doctor:  Can you tell me what problems you have had in the past. Or what medical issues are currently going on?\\nPatient: You mean my heart problem and things?\\nDoctor: Yes.\\nPatient: Okay, I have hypertension and kidney problems since many years.\\nDoctor: Okay.\\nPatient: I get frequent urinary infections.\\nDoctor: Your urine creatinine is normal.\\nPatient: Okay. I also have severe aortic stenosis.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['section_header'][:30]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-06T10:33:51.275997Z",
          "iopub.execute_input": "2024-06-06T10:33:51.276473Z",
          "iopub.status.idle": "2024-06-06T10:33:51.289996Z",
          "shell.execute_reply.started": "2024-06-06T10:33:51.276441Z",
          "shell.execute_reply": "2024-06-06T10:33:51.28866Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IG15jUA_puzv",
        "outputId": "2192e45c-0de2-4bcc-f680-3c2096349eb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['GENHX',\n",
              " 'GENHX',\n",
              " 'GENHX',\n",
              " 'MEDICATIONS',\n",
              " 'CC',\n",
              " 'PASTMEDICALHX',\n",
              " 'PASTMEDICALHX',\n",
              " 'ALLERGY',\n",
              " 'FAM/SOCHX',\n",
              " 'FAM/SOCHX',\n",
              " 'GENHX',\n",
              " 'GENHX',\n",
              " 'MEDICATIONS',\n",
              " 'PASTMEDICALHX',\n",
              " 'GENHX',\n",
              " 'PASTSURGICAL',\n",
              " 'GENHX',\n",
              " 'CC',\n",
              " 'GENHX',\n",
              " 'MEDICATIONS',\n",
              " 'PASTMEDICALHX',\n",
              " 'OTHER_HISTORY',\n",
              " 'PASTMEDICALHX',\n",
              " 'ALLERGY',\n",
              " 'ASSESSMENT',\n",
              " 'ASSESSMENT',\n",
              " 'CC',\n",
              " 'ALLERGY',\n",
              " 'GENHX',\n",
              " 'FAM/SOCHX']"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load base model(Mistral 7B)\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit= True,\n",
        "    bnb_4bit_quant_type= \"nf4\",\n",
        "    bnb_4bit_compute_dtype= torch.bfloat16,\n",
        "    bnb_4bit_use_double_quant= False,\n",
        ")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-06T10:33:55.227073Z",
          "iopub.execute_input": "2024-06-06T10:33:55.227497Z",
          "iopub.status.idle": "2024-06-06T10:33:55.236649Z",
          "shell.execute_reply.started": "2024-06-06T10:33:55.227459Z",
          "shell.execute_reply": "2024-06-06T10:33:55.235234Z"
        },
        "trusted": true,
        "id": "ONRgo1alpuzv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q git+https://github.com/huggingface/transformers\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-06-06T10:33:59.296013Z",
          "iopub.execute_input": "2024-06-06T10:33:59.297056Z",
          "iopub.status.idle": "2024-06-06T10:34:00.662689Z",
          "shell.execute_reply.started": "2024-06-06T10:33:59.296994Z",
          "shell.execute_reply": "2024-06-06T10:34:00.66048Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9LnnmN7xpuzv",
        "outputId": "6a61649a-a2d2-4467-f36c-116564f9b90f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import kagglehub"
      ],
      "metadata": {
        "id": "NIJUXSgDpuzv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Download latest version\n",
        "path = kagglehub.model_download(\"mistral-ai/mistral/pyTorch/7b-instruct-v0.1-hf\")\n",
        "\n",
        "print(\"Path to model files:\", path)"
      ],
      "metadata": {
        "id": "FKSFUI1i25EK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    base_model,\n",
        "    quantization_config=bnb_config,  # This handles the quantization configuration\n",
        "    torch_dtype=torch.bfloat16,\n",
        "    device_map=\"auto\",\n",
        "    trust_remote_code=True,\n",
        ")\n",
        "model.config.use_cache = False  # silence the warnings\n",
        "model.config.pretraining_tp = 1\n",
        "model.gradient_checkpointing_enable()"
      ],
      "metadata": {
        "id": "5WcJIquq3D_j",
        "outputId": "60390ef1-92a3-48a7-d9f5-49ad71f7741c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
          "referenced_widgets": [
            "9d793b5beea24f30ab794e937850c6b4",
            "6927dd025d074ec98976660eb5d4f02a",
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            "463da279bf0c4ba1b29fc2012ba31afc",
            "d17bdf92451641b89d6645afa516b8e7",
            "1e3c7c3ddbfd4328b869eeb178fe1708",
            "02bb7027953e463eadcbd93eb83d5725",
            "cd04ad4114ef4ec0b8eb4e40d4ad7bc7",
            "d960dd6efd7242598efaa84b2084a1aa",
            "05088b4c5a8d4accbe763c48b807e594",
            "e06383e24b44464682db464772448453",
            "4cea443a8b5b44dda7226521cf9c6146",
            "689b42e0e18845b2ab021172cc16b19b",
            "6e695a19480449359a02552c3d8375c3",
            "ed557590d57445f4af76d558510629ff",
            "8eed8747af5647c5a3ed2fa6ef33735d",
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            "5f000f4d9cb24acc9657dbbf42b53fd0",
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            "6eca5b53221e4e8a8bfacd039e5b8087",
            "976e7971bd95457bb8ef9160c5c09fe6",
            "bbcd25df104940a1b3c4638c2a058b9f",
            "cb2f3ac9ee4f4b6caf72b6c78d2f1474",
            "e217f414ebbb4b06ac28f07b6785202f",
            "a56924529a5b4042950291184b629211",
            "e7d32972142b42dab1ce8268e1eeba44",
            "f7fee521ef3c4bc3a9678eb5b38f0f96",
            "d967369ff0964116a2f13b4f36024953",
            "69fb95c240e14a05b2d4134815787670",
            "6f34e144719f4ef8bc749596b4b32e5f",
            "3f10749fb615449dbdf78355e822a388",
            "b6bc8b0d61ff41559dc22c7df0da2782",
            "76e5f9caf0e64ec8953857f716c01828",
            "4e13dcca26ca46ee94f52451c524da0f",
            "74daaf2c664148649db2582dc819931d",
            "4bf487b53a644c06805b8c5430abf78d",
            "be051e9c2f7e404886f911e5dddee259",
            "2ae576b3349147a29da360ce7696a221",
            "a03744aa40b7406c819c87ab428fd868",
            "0a422ddecd5e4b28b3e1d0917c9098dc",
            "67fc5cc74a8f4118a3f6ebcedc552b14",
            "4910866278fa472a936c48d92da4e1d7",
            "c6426ddb5fbe4badbce0edc09b0c4940",
            "988512adb4584cbeaca36f0e05acfde2",
            "fd264b79eddf466fb3890060646d8892",
            "7f835b7069184ba58bcce7d0125a6505",
            "71629bb0d72247a6838deecf494de88e",
            "e63ef0ab297a412eaaca13c409843b97"
          ]
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/25.1k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d793b5beea24f30ab794e937850c6b4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4cea443a8b5b44dda7226521cf9c6146"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/9.94G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7b21ea9e76be4246a77473509c53aeeb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00002.safetensors:   0%|          | 0.00/4.54G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "038a3495924a455c97831635b6c9c8c5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f7fee521ef3c4bc3a9678eb5b38f0f96"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2ae576b3349147a29da360ce7696a221"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    model = AutoModelForCausalLM.from_pretrained(\n",
        "        base_model,\n",
        "        quantization_config=bnb_config,\n",
        "        torch_dtype=torch.bfloat16,\n",
        "        device_map=\"auto\",\n",
        "        trust_remote_code=True,\n",
        "    )\n",
        "    print(\"Model loaded successfully!\")\n",
        "except Exception as e:\n",
        "    print(\"Failed to load model:\", str(e))"
      ],
      "metadata": {
        "id": "ZiOVcXGC3L_A",
        "outputId": "d8f614aa-1357-4c9d-b0ee-0e439a26d929",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "a0779928793c4eba9da66362092afe85",
            "7cc96b0a0cba4bc188e6d6cefc1c6de0",
            "2660d72f4e574e0aabe40862f760c601",
            "ba9e96029b774c908ee183e3df2cfbbc",
            "cfb412c8de6348208caf4d16485c6aef",
            "f0fe67bd5d02434a9ccd7eaa46bfa153",
            "f1132a5169fe475dbe0cadf0e296606d",
            "2713355e2ff944ada51e9a5ddb3b635e",
            "2cd57c3a12cf4a3cbba316af0aac010f",
            "63e630cf1ba04f218e594f781e271ed8",
            "493cf2563ff14777b3b9694ffb4ebe1b"
          ]
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a0779928793c4eba9da66362092afe85"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model loaded successfully!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)\n",
        "tokenizer.padding_side = 'right'\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.add_eos_token = True\n",
        "tokenizer.add_bos_token, tokenizer.add_eos_token"
      ],
      "metadata": {
        "id": "i74PdMXG4PfD",
        "outputId": "0e87f764-1d40-4209-8b73-75eedbd40ce6",
        "colab": {
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          "height": 163,
          "referenced_widgets": [
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            "2174b1a798e343669e1b9b35c1922a0a"
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        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.47k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]"
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              "tokenizer.json:   0%|          | 0.00/1.80M [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
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              "special_tokens_map.json:   0%|          | 0.00/72.0 [00:00<?, ?B/s]"
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        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(True, True)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Adding the adapters in the layers\n",
        "model = prepare_model_for_kbit_training(model)\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=16,\n",
        "    lora_dropout=0.1,\n",
        "    r=64,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\"gate_proj\"]\n",
        ")\n",
        "model = get_peft_model(model, peft_config)"
      ],
      "metadata": {
        "id": "9LehbZ1-4j3g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Hyperparamter\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    num_train_epochs=1,\n",
        "    per_device_train_batch_size=4,\n",
        "    gradient_accumulation_steps=1,\n",
        "    optim=\"paged_adamw_32bit\",\n",
        "    save_steps=25,\n",
        "    logging_steps=25,\n",
        "    learning_rate=2e-4,\n",
        "    weight_decay=0.001,\n",
        "    fp16=False,\n",
        "    bf16=False,\n",
        "    max_grad_norm=0.3,\n",
        "    max_steps=-1,\n",
        "    warmup_ratio=0.03,\n",
        "    group_by_length=True,\n",
        "     lr_scheduler_type=\"constant\",\n",
        "    report_to=\"wandb\"\n",
        "    )"
      ],
      "metadata": {
        "id": "S1JT0vgW4pMr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting sft parameters\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset,\n",
        "    peft_config=peft_config,\n",
        "    max_seq_length= None,\n",
        "    dataset_text_field=\"section_text\",\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        "    packing= False,\n",
        ")"
      ],
      "metadata": {
        "id": "BFJzLQmO4vDG",
        "outputId": "09a8d127-ccc9-4cdb-a946-0644847805e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265,
          "referenced_widgets": [
            "c48ceb64cb6242b58be9c57c5d830a0b",
            "fbc7f64e2122456793f4db65b24856ce",
            "5dda085e8073478c8e9ff711fcf2b6b5",
            "c946ad2d97724a2a8566146a277efe56",
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            "b6ab2f8f39634cf293c094e1ec0d76ab",
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            "f39c16836ebf4b45ac8196f45d0e2796",
            "aa15a2305cfe4931a29fff9048d3226e",
            "95ec2e09d19643d88069d6f2736db652"
          ]
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:100: FutureWarning: Deprecated argument(s) used in '__init__': dataset_text_field. Will not be supported from version '1.0.0'.\n",
            "\n",
            "Deprecated positional argument(s) used in SFTTrainer, please use the SFTConfig to set these arguments instead.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1965: FutureWarning: `--push_to_hub_token` is deprecated and will be removed in version 5 of 🤗 Transformers. Use `--hub_token` instead.\n",
            "  self.hub_token = self.push_to_hub_token\n",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:278: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:307: UserWarning: You passed a `dataset_text_field` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1301 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c48ceb64cb6242b58be9c57c5d830a0b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "id": "La-KninT4zsQ",
        "outputId": "d3265c83-6096-498b-af3c-4fd0a93e37f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
            "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='326' max='326' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [326/326 30:05, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>1.778700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.789300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>1.683800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.568800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>1.504500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>0.574400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175</td>\n",
              "      <td>1.518800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.575300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>225</td>\n",
              "      <td>1.494800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>0.583300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>275</td>\n",
              "      <td>1.432900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.510600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>325</td>\n",
              "      <td>0.997500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=326, training_loss=1.0752029544188202, metrics={'train_runtime': 1828.8358, 'train_samples_per_second': 0.711, 'train_steps_per_second': 0.178, 'total_flos': 4230096322338816.0, 'train_loss': 1.0752029544188202, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.model.save_pretrained(\"/kaggle/working/models/new_model\")"
      ],
      "metadata": {
        "id": "ovJsFqbE45xb",
        "outputId": "6a6c0cec-14d9-4ea6-da4e-c53d0a200794",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the fine-tuned model\n",
        "trainer.model.save_pretrained(new_model)\n",
        "wandb.finish()\n",
        "model.config.use_cache = True\n",
        "model.eval()"
      ],
      "metadata": {
        "id": "30n3-xiAAZmC",
        "outputId": "638f14d6-c8a7-4593-eaea-5078f0136c97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "23110e3094194c28a5557a2deda32fd7",
            "710666c6a08e4141bbef5fc8039908e7",
            "9da2bd21ce274b26b9d2eb6121703770",
            "ad0dfde8a23b4ac893e87458f22b96c6",
            "5eaa0514c711456388337ac45591a1e0",
            "1e51c7a5f4c34adea8016072c9ea92d1",
            "0b151b6fc39e4abb9d92931948b429c8",
            "b93889b8494e4e0a9dfe3ee072471418"
          ]
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(Label(value='0.002 MB of 0.002 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "23110e3094194c28a5557a2deda32fd7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>▁▂▂▃▃▄▄▅▆▆▇▇██</td></tr><tr><td>train/global_step</td><td>▁▂▂▃▃▄▄▅▆▆▇▇██</td></tr><tr><td>train/grad_norm</td><td>▁█▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train/learning_rate</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train/loss</td><td>█▃▇▁▆▁▇▁▆▁▆▁▄</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>total_flos</td><td>4230096322338816.0</td></tr><tr><td>train/epoch</td><td>1.0</td></tr><tr><td>train/global_step</td><td>326</td></tr><tr><td>train/grad_norm</td><td>0.25793</td></tr><tr><td>train/learning_rate</td><td>0.0002</td></tr><tr><td>train/loss</td><td>0.9975</td></tr><tr><td>train_loss</td><td>1.0752</td></tr><tr><td>train_runtime</td><td>1828.8358</td></tr><tr><td>train_samples_per_second</td><td>0.711</td></tr><tr><td>train_steps_per_second</td><td>0.178</td></tr></table><br/></div></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">warm-brook-1</strong> at: <a href='https://wandb.ai/solomon-odum/Fine%20tunig%20mistral%207b/runs/lxqbjfht' target=\"_blank\">https://wandb.ai/solomon-odum/Fine%20tunig%20mistral%207b/runs/lxqbjfht</a><br/> View project at: <a href='https://wandb.ai/solomon-odum/Fine%20tunig%20mistral%207b' target=\"_blank\">https://wandb.ai/solomon-odum/Fine%20tunig%20mistral%207b</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20240607_082146-lxqbjfht/logs</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PeftModelForCausalLM(\n",
              "  (base_model): LoraModel(\n",
              "    (model): MistralForCausalLM(\n",
              "      (model): MistralModel(\n",
              "        (embed_tokens): Embedding(32000, 4096)\n",
              "        (layers): ModuleList(\n",
              "          (0-31): 32 x MistralDecoderLayer(\n",
              "            (self_attn): MistralSdpaAttention(\n",
              "              (q_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=64, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=64, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (k_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=64, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=64, out_features=1024, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (v_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=64, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=64, out_features=1024, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (o_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=64, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=64, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (rotary_emb): MistralRotaryEmbedding()\n",
              "            )\n",
              "            (mlp): MistralMLP(\n",
              "              (gate_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=64, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=64, out_features=14336, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (up_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)\n",
              "              (down_proj): Linear4bit(in_features=14336, out_features=4096, bias=False)\n",
              "              (act_fn): SiLU()\n",
              "            )\n",
              "            (input_layernorm): MistralRMSNorm()\n",
              "            (post_attention_layernorm): MistralRMSNorm()\n",
              "          )\n",
              "        )\n",
              "        (norm): MistralRMSNorm()\n",
              "      )\n",
              "      (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logging.set_verbosity(logging.CRITICAL)\n",
        "\n",
        "prompt = \" summarize the symptoms  in this dialogue: 'Doctor: What brings you back into the clinic today, miss? Patient: I came in for a refill of my blood pressure medicine. Doctor: It looks like Doctor Kumar followed up with you last time regarding your hypertension, osteoarthritis, osteoporosis, hypothyroidism, allergic rhinitis and kidney stones. Have you noticed any changes or do you have any concerns regarding these issues? Patient: No. Doctor: Have you had any fever or chills, cough, congestion, nausea, vomiting, chest pain, chest pressure? Patient: No. Doctor: Great. Also, for our records, how old are you and what race do you identify yourself as? Patient: I am seventy six years old and identify as a white female'\"\n",
        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=1024)\n",
        "result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n",
        "print(result[0]['generated_text'])"
      ],
      "metadata": {
        "id": "TeOzo9IoAeyi",
        "outputId": "8b984ab8-05a0-4ada-b11f-2b86f476852f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s>[INST]  summarize the symptoms  in this dialogue: 'Doctor: What brings you back into the clinic today, miss? Patient: I came in for a refill of my blood pressure medicine. Doctor: It looks like Doctor Kumar followed up with you last time regarding your hypertension, osteoarthritis, osteoporosis, hypothyroidism, allergic rhinitis and kidney stones. Have you noticed any changes or do you have any concerns regarding these issues? Patient: No. Doctor: Have you had any fever or chills, cough, congestion, nausea, vomiting, chest pain, chest pressure? Patient: No. Doctor: Great. Also, for our records, how old are you and what race do you identify yourself as? Patient: I am seventy six years old and identify as a white female' [/INST] The patient came into the clinic for a refill of her blood pressure medication. The doctor reviewed her medical history, including hypertension, osteoarthritis, osteoporosis, hypothyroidism, allergic rhinitis, and kidney stones. The patient reported no changes or concerns regarding these issues. The doctor also asked about any recent symptoms such as fever, chills, cough, congestion, nausea, vomiting, chest pain, or chest pressure. The patient reported no symptoms of these nature. Finally, the doctor asked for the patient's age and race, and the patient reported being 76 years old and identifying as a white female.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \" what symptoms did the patient present in this dialogue?: 'Doctor: What brings you back into the clinic today, miss? Patient: I came in for a refill of my blood pressure medicine. Doctor: It looks like Doctor Kumar followed up with you last time regarding your hypertension, osteoarthritis, osteoporosis, hypothyroidism, allergic rhinitis and kidney stones. Have you noticed any changes or do you have any concerns regarding these issues? Patient: No. Doctor: Have you had any fever or chills, cough, congestion, nausea, vomiting, chest pain, chest pressure? Patient: No. Doctor: Great. Also, for our records, how old are you and what race do you identify yourself as? Patient: I am seventy six years old and identify as a white female'\"\n",
        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=1024)\n",
        "result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n",
        "print(result[0]['generated_text'])"
      ],
      "metadata": {
        "id": "on45qMNbBG_e",
        "outputId": "3fc38bad-8364-45b8-bed7-fbd46ec9b2e8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s>[INST]  what symptoms did the patient present in this dialogue?: 'Doctor: What brings you back into the clinic today, miss? Patient: I came in for a refill of my blood pressure medicine. Doctor: It looks like Doctor Kumar followed up with you last time regarding your hypertension, osteoarthritis, osteoporosis, hypothyroidism, allergic rhinitis and kidney stones. Have you noticed any changes or do you have any concerns regarding these issues? Patient: No. Doctor: Have you had any fever or chills, cough, congestion, nausea, vomiting, chest pain, chest pressure? Patient: No. Doctor: Great. Also, for our records, how old are you and what race do you identify yourself as? Patient: I am seventy six years old and identify as a white female' [/INST] The patient presented with the following symptoms in this dialogue:\n",
            "- Hypertension\n",
            "- Osteoarthritis\n",
            "- Osteoporosis\n",
            "- Hypothyroidism\n",
            "- Allergic rhinitis\n",
            "- Kidney stones\n",
            "- No fever or chills\n",
            "- No cough\n",
            "- No congestion\n",
            "- No nausea\n",
            "- No vomiting\n",
            "- No chest pain\n",
            "- No chest pressure\n",
            "- No other reported symptoms.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \" what are the symptoms of the patient: 'Doctor: What brings you back into the clinic today, miss? Patient: I came in for a refill of my blood pressure medicine. Doctor: It looks like Doctor Kumar followed up with you last time regarding your hypertension, osteoarthritis, osteoporosis, hypothyroidism, allergic rhinitis and kidney stones. Have you noticed any changes or do you have any concerns regarding these issues? Patient: No. Doctor: Have you had any fever or chills, cough, congestion, nausea, vomiting, chest pain, chest pressure? Patient: No. Doctor: Great. Also, for our records, how old are you and what race do you identify yourself as? Patient: I am seventy six years old and identify as a white female'\"\n",
        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=1024)\n",
        "result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n",
        "print(result[0]['generated_text'])"
      ],
      "metadata": {
        "id": "84MCe6tIA-gG",
        "outputId": "3db0d6a6-b5cd-4925-9cd5-f8b8097b4e53",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s>[INST]  what are the symptoms of the patient: 'Doctor: What brings you back into the clinic today, miss? Patient: I came in for a refill of my blood pressure medicine. Doctor: It looks like Doctor Kumar followed up with you last time regarding your hypertension, osteoarthritis, osteoporosis, hypothyroidism, allergic rhinitis and kidney stones. Have you noticed any changes or do you have any concerns regarding these issues? Patient: No. Doctor: Have you had any fever or chills, cough, congestion, nausea, vomiting, chest pain, chest pressure? Patient: No. Doctor: Great. Also, for our records, how old are you and what race do you identify yourself as? Patient: I am seventy six years old and identify as a white female' [/INST] The patient's symptoms include hypertension, osteoarthritis, osteoporosis, hypothyroidism, allergic rhinitis, and kidney stones. The patient reports no changes or concerns regarding these issues. The patient also reports no fever, chills, cough, congestion, nausea, vomiting, chest pain, or chest pressure. The patient is seventy six years old and identifies as a white female.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5Pv1MIynB5HQ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}