colabs/AutoTrain_LLM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/stevenbowler/LamdaLabsTest/blob/master/colabs/AutoTrain_LLM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "JvMRbVLEJlZT",
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ad22b35-0cec-4bf7-c604-76940c677847"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/bin/autotrain\", line 5, in <module>\n",
            "    from autotrain.cli.autotrain import main\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/autotrain/cli/autotrain.py\", line 10, in <module>\n",
            "    from .run_spacerunner import RunAutoTrainSpaceRunnerCommand\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/autotrain/cli/run_spacerunner.py\", line 3, in <module>\n",
            "    from autotrain.backend import SpaceRunner\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/autotrain/backend.py\", line 11, in <module>\n",
            "    from autotrain.dataset import AutoTrainDataset, AutoTrainDreamboothDataset\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/autotrain/dataset.py\", line 7, in <module>\n",
            "    import pandas as pd\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/__init__.py\", line 48, in <module>\n",
            "    from pandas.core.api import (\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/api.py\", line 27, in <module>\n",
            "    from pandas.core.arrays import Categorical\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/arrays/__init__.py\", line 1, in <module>\n",
            "    from pandas.core.arrays.arrow import ArrowExtensionArray\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/arrays/arrow/__init__.py\", line 1, in <module>\n",
            "    from pandas.core.arrays.arrow.array import ArrowExtensionArray\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/arrays/arrow/array.py\", line 52, in <module>\n",
            "    import pyarrow.compute as pc\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pyarrow/compute.py\", line 18, in <module>\n",
            "    from pyarrow._compute import (  # noqa\n",
            "  File \"<frozen importlib._bootstrap>\", line 404, in parent\n",
            "KeyboardInterrupt\n",
            "^C\n"
          ]
        }
      ],
      "source": [
        "#@title 🤗 AutoTrain LLM\n",
        "#@markdown In order to use this colab\n",
        "#@markdown - upload train.csv to a folder named `data/`\n",
        "#@markdown - train.csv must contain a `text` column\n",
        "#@markdown - choose a project name if you wish\n",
        "#@markdown - change model if you wish, you can use most of the text-generation models from Hugging Face Hub\n",
        "#@markdown - add huggingface information (token and repo_id) if you wish to push trained model to huggingface hub\n",
        "#@markdown - update hyperparameters if you wish\n",
        "#@markdown - click `Runtime > Run all` or run each cell individually\n",
        "\n",
        "import os\n",
        "!pip install -U autotrain-advanced > install_logs.txt\n",
        "!autotrain setup > setup_logs.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vt1FBYAJSsVR",
        "outputId": "01567468-2601-4dec-8f77-48dd9f2e3f3a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "B_fMIcEeTI_b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76b28fa1-9aa6-4577-e40a-53987b7f19d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cp: cannot create regular file '/content/data/train.csv': No such file or directory\n"
          ]
        }
      ],
      "source": [
        "!cp \"/content/drive/MyDrive/ColabStuff/train.csv\" \"/content/data/train.csv\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "aM0GB6XAVWWR"
      },
      "outputs": [],
      "source": [
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "A2-_lkBS1WKA"
      },
      "outputs": [],
      "source": [
        "#@markdown ---\n",
        "#@markdown #### Project Config\n",
        "#@markdown Note: if you are using a restricted/private model, you need to enter your Hugging Face token in the next step.\n",
        "project_name = 'FineTuneLLaMa2' # @param {type:\"string\"}\n",
        "model_name = 'TinyPixel/Llama-2-7B-bf16-sharded' # @param {type:\"string\"}\n",
        "\n",
        "#@markdown ---\n",
        "#@markdown #### Push to Hub?\n",
        "#@markdown Use these only if you want to push your trained model to a private repo in your Hugging Face Account\n",
        "#@markdown If you dont use these, the model will be saved in Google Colab and you are required to download it manually.\n",
        "#@markdown Please enter your Hugging Face write token. The trained model will be saved to your Hugging Face account.\n",
        "#@markdown You can find your token here: https://huggingface.co/settings/tokens\n",
        "push_to_hub = True # @param [\"False\", \"True\"] {type:\"raw\"}\n",
        "hf_token = \"\" #@param {type:\"string\"}\n",
        "repo_id = \"stevenbowler/MedChatBot4\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown ---\n",
        "#@markdown #### Hyperparameters\n",
        "learning_rate = 2e-4 # @param {type:\"number\"}\n",
        "num_epochs = 1 #@param {type:\"number\"}\n",
        "batch_size = 4 # @param {type:\"slider\", min:1, max:32, step:1}\n",
        "block_size = 512 # @param {type:\"number\"}\n",
        "trainer = \"sft\" # @param [\"default\", \"sft\"] {type:\"raw\"}\n",
        "warmup_ratio = 0.1 # @param {type:\"number\"}\n",
        "weight_decay = 0.01 # @param {type:\"number\"}\n",
        "gradient_accumulation = 4 # @param {type:\"number\"}\n",
        "use_fp16 = True # @param [\"False\", \"True\"] {type:\"raw\"}\n",
        "use_peft = True # @param [\"False\", \"True\"] {type:\"raw\"}\n",
        "use_int4 = True # @param [\"False\", \"True\"] {type:\"raw\"}\n",
        "lora_r = 16 #@param {type:\"number\"}\n",
        "lora_alpha = 32 #@param {type:\"number\"}\n",
        "lora_dropout = 0.05 #@param {type:\"number\"}\n",
        "\n",
        "os.environ[\"PROJECT_NAME\"] = project_name\n",
        "os.environ[\"MODEL_NAME\"] = model_name\n",
        "os.environ[\"PUSH_TO_HUB\"] = str(push_to_hub)\n",
        "os.environ[\"HF_TOKEN\"] = hf_token\n",
        "os.environ[\"REPO_ID\"] = repo_id\n",
        "os.environ[\"LEARNING_RATE\"] = str(learning_rate)\n",
        "os.environ[\"NUM_EPOCHS\"] = str(num_epochs)\n",
        "os.environ[\"BATCH_SIZE\"] = str(batch_size)\n",
        "os.environ[\"BLOCK_SIZE\"] = str(block_size)\n",
        "os.environ[\"WARMUP_RATIO\"] = str(warmup_ratio)\n",
        "os.environ[\"WEIGHT_DECAY\"] = str(weight_decay)\n",
        "os.environ[\"GRADIENT_ACCUMULATION\"] = str(gradient_accumulation)\n",
        "os.environ[\"USE_FP16\"] = str(use_fp16)\n",
        "os.environ[\"USE_PEFT\"] = str(use_peft)\n",
        "os.environ[\"USE_INT4\"] = str(use_int4)\n",
        "os.environ[\"LORA_R\"] = str(lora_r)\n",
        "os.environ[\"LORA_ALPHA\"] = str(lora_alpha)\n",
        "os.environ[\"LORA_DROPOUT\"] = str(lora_dropout)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z1Vf-RMGKjis",
        "outputId": "01ee98b3-c7c0-4090-8db4-ddab3288b0b9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "push_to_hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-jkBfwgCKjis"
      },
      "outputs": [],
      "source": [
        "os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M2GijjpqKjis"
      },
      "outputs": [],
      "source": [
        "os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:256'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "\n",
        "hf_token=getpass.getpass('HuggingFace Write Key: ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jbgk-ETlQBek",
        "outputId": "b5c1c5ff-b9e2-44d4-88e3-c80dd6275b55"
      },
      "execution_count": 16,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "HuggingFace Write Key: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5arVTjkhV_og"
      },
      "outputs": [],
      "source": [
        "# !autotrain setup --update-torch   # recommended for colab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "g3cd_ED_yXXt",
        "tags": []
      },
      "outputs": [],
      "source": [
        "#failed due to last 4 variables not being set, syntax\n",
        "# !autotrain llm \\\n",
        "# --train \\\n",
        "# --model ${MODEL_NAME} \\\n",
        "# --project-name ${PROJECT_NAME} \\\n",
        "# --data-path data/ \\\n",
        "# --text-column text \\\n",
        "# --lr ${LEARNING_RATE} \\\n",
        "# --batch-size ${BATCH_SIZE} \\\n",
        "# --epochs ${NUM_EPOCHS} \\\n",
        "# --block-size ${BLOCK_SIZE} \\\n",
        "# --warmup-ratio ${WARMUP_RATIO} \\\n",
        "# --lora-r ${LORA_R} \\\n",
        "# --lora-alpha ${LORA_ALPHA} \\\n",
        "# --lora-dropout ${LORA_DROPOUT} \\\n",
        "# --weight-decay ${WEIGHT_DECAY} \\\n",
        "# --gradient-accumulation ${GRADIENT_ACCUMULATION} \\\n",
        "# $( [[ \"$USE_FP16\" == \"True\" ]] && echo \"--fp16\" ) \\\n",
        "# $( [[ \"$USE_PEFT\" == \"True\" ]] && echo \"--use-peft\" ) \\\n",
        "# $( [[ \"$USE_INT4\" == \"True\" ]] && echo \"--use-int4\" ) \\\n",
        "# $( [[ \"$PUSH_TO_HUB\" == \"True\" ]] && echo \"--push-to-hub --token ${HF_TOKEN} --repo-id ${REPO_ID}\" )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lMES43mRKjit",
        "outputId": "5a639928-8c7c-464c-c939-013e162ac3bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.16.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.12.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.65.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.5.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (23.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2023.7.22)\n"
          ]
        }
      ],
      "source": [
        "!pip install huggingface_hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168,
          "referenced_widgets": [
            "0a058c3fe6434f639ebb6159df07349d",
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            "5c79679871674273b07f9514ad71b115",
            "1cd854151e1a4c76ba3a232562b5a19c",
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            "66faf875c6764e79806912fc6b225932",
            "d06973f590f04941b04d16b750a21b15",
            "88e4b322a6e640a48c247cb09e122fd7",
            "e87a0acced064fa7a6e4ea6153e88a85",
            "acbcfcf015804f30a508ea595f4f672a",
            "4c944afbad5044479ab8f4e9a65ef500",
            "d924f68780a04eaabfe04182119211ac",
            "6c88ceecf5a8463ba17cf1d6304053eb"
          ]
        },
        "id": "QTiUbwflKjit",
        "outputId": "3feac674-770b-4d94-eab9-c30756b5fff3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a058c3fe6434f639ebb6159df07349d"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# connect to huggingface\n",
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C9-uzAVlKjiu",
        "outputId": "f8c2d035-6e8b-441a-c8ed-ed7e193898b7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--------------------------------------------------------------------------\n",
            "WARNING: No preset parameters were found for the device that Open MPI\n",
            "detected:\n",
            "\n",
            "  Local host:            129-159-39-145\n",
            "  Device name:           mlx5_0\n",
            "  Device vendor ID:      0x02c9\n",
            "  Device vendor part ID: 4126\n",
            "\n",
            "Default device parameters will be used, which may result in lower\n",
            "performance.  You can edit any of the files specified by the\n",
            "btl_openib_device_param_files MCA parameter to set values for your\n",
            "device.\n",
            "\n",
            "NOTE: You can turn off this warning by setting the MCA parameter\n",
            "      btl_openib_warn_no_device_params_found to 0.\n",
            "--------------------------------------------------------------------------\n",
            "--------------------------------------------------------------------------\n",
            "No OpenFabrics connection schemes reported that they were able to be\n",
            "used on a specific port.  As such, the openib BTL (OpenFabrics\n",
            "support) will be disabled for this port.\n",
            "\n",
            "  Local host:           129-159-39-145\n",
            "  Local device:         mlx5_0\n",
            "  Local port:           1\n",
            "  CPCs attempted:       udcm\n",
            "--------------------------------------------------------------------------\n",
            "/home/ubuntu/.local/lib/python3.8/site-packages/pandas/core/computation/expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).\n",
            "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n",
            "/usr/lib/python3/dist-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: /usr/lib/python3/dist-packages/torchvision/image.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE\n",
            "  warn(f\"Failed to load image Python extension: {e}\")\n",
            "> \u001b[1mINFO    Running LLM\u001b[0m\n",
            "> \u001b[1mINFO    Params: Namespace(add_eos_token=False, auto_find_batch_size=False, backend='default', block_size=512, data_path='data/', deploy=False, evaluation_strategy='epoch', fp16=True, func=<function run_llm_command_factory at 0x7f1fbc26e4c0>, gradient_accumulation_steps=4, inference=False, learning_rate=0.0002, logging_steps=-1, lora_alpha=32, lora_dropout=0.05, lora_r=16, max_grad_norm=1.0, merge_adapter=False, model='TinyPixel/Llama-2-7B-bf16-sharded', model_max_length=1024, num_train_epochs=1, optimizer='adamw_torch', project_name='FineTuneLLaMa2', push_to_hub=True, repo_id='stevenbowler/MedChatBot4', save_strategy='epoch', save_total_limit=1, scheduler='linear', seed=42, target_modules=None, text_column='text', token='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', train=True, train_batch_size=4, train_split='train', trainer='default', use_int4=True, use_int8=False, use_peft=True, username=None, valid_split=None, version=False, warmup_ratio=0.1, weight_decay=0.01)\u001b[0m\n",
            "> \u001b[1mINFO    loading dataset from csv\u001b[0m\n",
            "/home/ubuntu/.local/lib/python3.8/site-packages/transformers/models/auto/tokenization_auto.py:640: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n",
            "Using pad_token, but it is not set yet.\n",
            "/home/ubuntu/.local/lib/python3.8/site-packages/transformers/models/auto/configuration_auto.py:1006: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n",
            "/home/ubuntu/.local/lib/python3.8/site-packages/transformers/models/auto/auto_factory.py:471: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n",
            "Loading checkpoint shards: 100%|████████████████| 14/14 [00:11<00:00,  1.18it/s]\n",
            "/home/ubuntu/.local/lib/python3.8/site-packages/transformers/utils/hub.py:374: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n",
            "You are resizing the embedding layer without providing a `pad_to_multiple_of` parameter. This means that the new embedding dimension will be 32000. This might induce some performance reduction as *Tensor Cores* will not be available. For more details about this, or help on choosing the correct value for resizing, refer to this guide: https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc\n",
            "Running tokenizer on train dataset:  88%|▉| 9000/10178 [00:00<00:00, 10751.49 exToken indices sequence length is longer than the specified maximum sequence length for this model (1121 > 1024). Running this sequence through the model will result in indexing errors\n",
            "Running tokenizer on train dataset: 100%|█| 10178/10178 [00:00<00:00, 10480.74 e\n",
            "Grouping texts in chunks of 512 (num_proc=4): 100%|█| 10178/10178 [00:00<00:00, \n",
            "> \u001b[1mINFO    creating trainer\u001b[0m\n",
            " 76%|███████████████████████████████▎         | 203/266 [38:25<11:57, 11.40s/it]"
          ]
        }
      ],
      "source": [
        "!autotrain llm \\\n",
        "--train \\\n",
        "--model ${MODEL_NAME} \\\n",
        "--project-name ${PROJECT_NAME} \\\n",
        "--data-path data/ \\\n",
        "--text-column text \\\n",
        "--lr ${LEARNING_RATE} \\\n",
        "--batch-size ${BATCH_SIZE} \\\n",
        "--epochs ${NUM_EPOCHS} \\\n",
        "--block-size ${BLOCK_SIZE} \\\n",
        "--warmup-ratio ${WARMUP_RATIO} \\\n",
        "--lora-r ${LORA_R} \\\n",
        "--lora-alpha ${LORA_ALPHA} \\\n",
        "--lora-dropout ${LORA_DROPOUT} \\\n",
        "--weight-decay ${WEIGHT_DECAY} \\\n",
        "--gradient-accumulation ${GRADIENT_ACCUMULATION} \\\n",
        "--fp16 \\\n",
        "--use-peft \\\n",
        "--use-int4 \\\n",
        "--push-to-hub --token ${HF_TOKEN} --repo-id ${REPO_ID}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "G2sszHW9Kjiu"
      },
      "outputs": [],
      "source": [
        "adapter_model_name = \"stevenbowler/MedChatBot4\"\n",
        "model_name = 'TinyPixel/Llama-2-7B-bf16-sharded'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "HhPhYRPeOGNH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "4whMjFDtS23i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UqH61eKmS4Tw",
        "outputId": "c35f1bab-b65c-4d92-e466-1fd4e760bae2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.34.0.dev0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.16.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.15,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.14.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.3.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM\n",
        "\n",
        "adapter_model = AutoModelForCausalLM.from_pretrained(adapter_model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "4af2ef0548394f5dbc7dc3130d146f67",
            "344796e5e8c244f6a623d489a0736dc4",
            "68d92c1e182d4f0d9cee84114f27b2f3",
            "c45a1decc9154a0ca03135b391b7421d",
            "bdd8b4cd3b44401c99dd9bc433e8044e",
            "3d6126ec17a346d785d01bf1c0b0280b",
            "e9e49579967141e6936aa907b3a7ba5a",
            "9ff705448ca44a29b06febe859342157",
            "a33799f4f676491087b66155b6277759",
            "d813c8c4872f42db8aa4f37ca9002fe6",
            "b0a4a1ccc900470aa8227293823c2d62",
            "2e492a6dfb82451ea655c4fda20e5134",
            "e4699a4e1061492f9c1872ecfb00c9c7",
            "f2c8982f8a04490ea4914ce7f2fcb421",
            "7ac53364711f47dc9c623f2211fdcce1",
            "8b65d551851346a8a052a199afa7d730",
            "01351d0e4c1e4fa6a48d505a327649e7",
            "8aaeee34dc2940dfb91f3e1ba10972e1",
            "37881de5ffba43a686247c44c653fa1c",
            "c41e75e2464747a2a6161d33b5846835",
            "49e7b4dedd68487b9a8c5f708fa8a76b",
            "dcb5f74305d94f9cb17f2cd2ad68d45b"
          ]
        },
        "id": "fF9M-GTCOI5v",
        "outputId": "32409836-11ce-426c-8e7d-11c8edcbce79"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/14 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4af2ef0548394f5dbc7dc3130d146f67"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading adapter_model.bin:   0%|          | 0.00/33.6M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2e492a6dfb82451ea655c4fda20e5134"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from torch import cuda, bfloat16\n",
        "import transformers\n",
        "\n",
        "bnb_config = transformers.BitsAndBytesConfig(\n",
        "    load_in_8bit=True,\n",
        "    # bnb_4bit_quant_type='nf4',\n",
        "    # bnb_4bit_use_double_quant=True,\n",
        "    # bnb_4bit_compute_dtype=bfloat16\n",
        ")\n",
        "\n",
        "model_config = transformers.AutoConfig.from_pretrained(\n",
        "    model_name,\n",
        "    gradient_checkpointing=True,\n",
        "    use_auth_token=hf_token\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "      model_name,\n",
        "      trust_remote_code=True,\n",
        "      config=model_config,\n",
        "      quantization_config=bnb_config,\n",
        "      device_map='auto',\n",
        "      use_auth_token=hf_token\n",
        "      )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173,
          "referenced_widgets": [
            "a118963ad86d47368c3184c2dfdce7ac",
            "ff5a087d6d7b4a528d487b492397b2a8",
            "63e89d934b2d466daa65f9888b714201",
            "e774b532b9d64727b9d213be010b9f15",
            "70172f44be6040c59bb0072a50d725d1",
            "9dc9a35a9b4143988b52c5f37c3d10a1",
            "9a2cfabb3c2d4ccc9fc4e4c12e05e9d7",
            "5e33a7620560457fb2f77dfc90bc83c7",
            "7a9bb6027be34a83b596aaea6b5485c4",
            "26a8121a38ea4fdd99f5b7cf3154a2d9",
            "19a81f42040f4c1498ee7227c1258722"
          ]
        },
        "id": "XnQUkVKDVcO8",
        "outputId": "74bf6377-bc72-4b88-a358-5bd0488132b8"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py:1015: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py:479: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/14 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a118963ad86d47368c3184c2dfdce7ac"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py:374: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import PeftModel\n",
        "\n",
        "model = PeftModel.from_pretrained(model, adapter_model_name)"
      ],
      "metadata": {
        "id": "-gAvD-atOJgK"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = model.merge_and_unload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bUxpa4W6awbB",
        "outputId": "6c606e88-48af-42a1-d716-d3023d32f313"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/tuners/lora/bnb.py:68: UserWarning: Merge lora module to 8-bit linear may get different generations due to rounding errors.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_model_name = \"stevenbowler/MedChatBotAdapted\"\n",
        "\n",
        "model.push_to_hub(new_model_name, use_auth_token=True, max_shard_size=\"5GB\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310,
          "referenced_widgets": [
            "1348cf9536164ad4811cb2adeb46bf02",
            "3e9cf3cb268841938d73fdaff6bffd66",
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            "a131609d611642b8afc2bd79f0853b9f",
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            "99691f61d47a4fe2a7fdf9677ad42f5c",
            "60ca1aa6edd34899bc07108e7afc1246",
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            "d2d2d3d6802045129908230366b02643",
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            "bef8f9d0a02c4957b31dfe919d15bccc",
            "7b320bfe320742c1b67378863c37863e",
            "2c0322a4aabd4e74b579d0dc4e9e2aa5",
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            "42b9e5c6355d434994ade00a60b10bd9"
          ]
        },
        "id": "FDocnkG6a8Gg",
        "outputId": "68eaa4ee-82c4-45e6-d036-3c8c1ff11ea7"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py:844: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:1938: UserWarning: You are calling `save_pretrained` to a 8-bit converted model you may likely encounter unexepected behaviors. If you want to save 8-bit models, make sure to have `bitsandbytes>0.37.2` installed.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1348cf9536164ad4811cb2adeb46bf02"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model-00001-of-00002.bin:   0%|          | 0.00/4.99G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "99691f61d47a4fe2a7fdf9677ad42f5c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model-00002-of-00002.bin:   0%|          | 0.00/2.02G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/stevenbowler/MedChatBotAdapted/commit/0a9bbea3ab15e6ae15535275f0654f5e805ded13', commit_message='Upload LlamaForCausalLM', commit_description='', oid='0a9bbea3ab15e6ae15535275f0654f5e805ded13', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer_model_name = model_name\n",
        "\n",
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(tokenizer_model_name, use_auth_token=hf_token)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245,
          "referenced_widgets": [
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        },
        "id": "lvooR0e1tMSt",
        "outputId": "2f1c4835-be9c-4237-b230-8d26837dcaec"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/tokenization_auto.py:648: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/676 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce1b54b9464a4cf7bf12e7a0b361d00a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
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