Fine_Tune_Llama2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "viqfyuWR-xfu",
    "outputId": "a2d12d0b-ee2f-495f-f9e6-68c702180b0f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: tokenizers, safetensors, xxhash, dill, multiprocess, huggingface-hub, transformers, datasets, accelerate, trl, peft\n",
      "Successfully installed accelerate-0.21.0 datasets-2.14.3 dill-0.3.7 huggingface-hub-0.16.4 multiprocess-0.70.15 peft-0.4.0 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.31.0 trl-0.5.0 xxhash-3.3.0\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers peft trl datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VkyVfwdTBsXo",
    "outputId": "4f530813-a1d8-487d-8590-aef39f95873c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting einops\n",
      "  Downloading einops-0.6.1-py3-none-any.whl (42 kB)\n",
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      "\u001b[?25hInstalling collected packages: einops\n",
      "Successfully installed einops-0.6.1\n"
     ]
    }
   ],
   "source": [
    "!pip install einops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R6JfWRaLB5bG",
    "outputId": "ed28448a-e559-44d7-c079-a3357749fb2b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: bitsandbytes\n",
      "Successfully installed bitsandbytes-0.41.1\n"
     ]
    }
   ],
   "source": [
    "!pip install bitsandbytes accelerate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "L_Uu20E-DgfA"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import gc\n",
    "import pandas as pd\n",
    "from datasets import Dataset\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig,AutoTokenizer\n",
    "from transformers import TrainingArguments,pipeline\n",
    "from peft import LoraConfig, PeftModel, get_peft_config\n",
    "from trl import SFTTrainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4RmSnMiXvX_s"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EizsZLEiCa7F"
   },
   "outputs": [],
   "source": [
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_use_double_quant=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=torch.bfloat16\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZtxAzDgkD1nb"
   },
   "outputs": [],
   "source": [
    "device_map = \"auto\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lh3TCuouEX59"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/medquad.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8r7OhCBOl0QE",
    "outputId": "b3283ed2-f84d-4ce4-96c9-293bf5d1f13c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16412, 4)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "wwpiZnLfqY1i",
    "outputId": "c376d81f-14c7-4ee7-936e-8cef298e3a25"
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>What is (are) Glaucoma ?</td>\n",
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       "      <th>3</th>\n",
       "      <td>What are the treatments for Glaucoma ?</td>\n",
       "      <td>Although open-angle glaucoma cannot be cured, ...</td>\n",
       "      <td>NIHSeniorHealth</td>\n",
       "      <td>Glaucoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>What is (are) Glaucoma ?</td>\n",
       "      <td>Glaucoma is a group of diseases that can damag...</td>\n",
       "      <td>NIHSeniorHealth</td>\n",
       "      <td>Glaucoma</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de1dfd1a-1a28-4497-a1cb-8b7fe95fbe8b')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "\n",
       "\n",
       "\n",
       "    <div id=\"df-ef9f8fad-9c6a-4834-86f2-e68d6b5e8a47\">\n",
       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ef9f8fad-9c6a-4834-86f2-e68d6b5e8a47')\"\n",
       "              title=\"Suggest charts.\"\n",
       "              style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "      </button>\n",
       "    </div>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "    background-color: #E8F0FE;\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: #1967D2;\n",
       "    height: 32px;\n",
       "    padding: 0 0 0 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: #E2EBFA;\n",
       "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: #174EA6;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "    background-color: #3B4455;\n",
       "    fill: #D2E3FC;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart:hover {\n",
       "    background-color: #434B5C;\n",
       "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "    fill: #FFFFFF;\n",
       "  }\n",
       "</style>\n",
       "\n",
       "    <script>\n",
       "      async function quickchart(key) {\n",
       "        const containerElement = document.querySelector('#' + key);\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      }\n",
       "    </script>\n",
       "\n",
       "      <script>\n",
       "\n",
       "function displayQuickchartButton(domScope) {\n",
       "  let quickchartButtonEl =\n",
       "    domScope.querySelector('#df-ef9f8fad-9c6a-4834-86f2-e68d6b5e8a47 button.colab-df-quickchart');\n",
       "  quickchartButtonEl.style.display =\n",
       "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "}\n",
       "\n",
       "        displayQuickchartButton(document);\n",
       "      </script>\n",
       "      <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-de1dfd1a-1a28-4497-a1cb-8b7fe95fbe8b button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-de1dfd1a-1a28-4497-a1cb-8b7fe95fbe8b');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                                 question  \\\n",
       "0                What is (are) Glaucoma ?   \n",
       "1                  What causes Glaucoma ?   \n",
       "2     What are the symptoms of Glaucoma ?   \n",
       "3  What are the treatments for Glaucoma ?   \n",
       "4                What is (are) Glaucoma ?   \n",
       "\n",
       "                                              answer           source  \\\n",
       "0  Glaucoma is a group of diseases that can damag...  NIHSeniorHealth   \n",
       "1  Nearly 2.7 million people have glaucoma, a lea...  NIHSeniorHealth   \n",
       "2  Symptoms of Glaucoma  Glaucoma can develop in ...  NIHSeniorHealth   \n",
       "3  Although open-angle glaucoma cannot be cured, ...  NIHSeniorHealth   \n",
       "4  Glaucoma is a group of diseases that can damag...  NIHSeniorHealth   \n",
       "\n",
       "  focus_area  \n",
       "0   Glaucoma  \n",
       "1   Glaucoma  \n",
       "2   Glaucoma  \n",
       "3   Glaucoma  \n",
       "4   Glaucoma  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FetlRCYnsde4"
   },
   "outputs": [],
   "source": [
    "data = Dataset.from_pandas(pd.DataFrame(data=df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "N1yjRRODD5xM"
   },
   "outputs": [],
   "source": [
    "model_name = \"NousResearch/Llama-2-7b-chat-hf\"\n",
    "#\"daryl149/llama-2-7b-chat-hf\"\n",
    "#model_name = \"meta-llama/Llama-2-7b-hf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 177,
     "referenced_widgets": [
      "de05a5d909ef44a78b90527c9b21ed5a",
      "bdbaa07a3fe448a4b69697632e08c236",
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      "c43ada216df8494f9650468a863cb8ae",
      "8e9dc6f0f0fd47b79f184891d493e626",
      "bacd10bd88f14b6398cdff1922466a87",
      "0cb5de4cf2bf4448b6440a596fa06e1a",
      "7714986031504feabe7bd2bea2fd8cce",
      "31ffaebf64384a6c9578f164844cf730",
      "6de942b9d58847a9bc96cbb943feed67",
      "22870baedd8241e089a83a6164be6d46",
      "2a6e3aad196c439690e8d2eef6e6f4dd",
      "e788da1d071a48729cfb19934d4ba275",
      "85a278045f494c4c9fe391f2482f929a",
      "33dadb18642e4f13afe77992b4db5ecd",
      "97b3079175d245a5b073563a3c083c3d",
      "69c7aa60953b433eb03c33c0afe4fe91",
      "123c1b91c6b6469ead8e2e660a6f17a5",
      "02f3f7dd8db24885afe187e8eb03f31b",
      "da80348b11d442fda831748ec9dafa56",
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      "714c5a21ae4c4dd89c9c4c7a2787c8ed",
      "b9b3d214b3604fe3aac8f3c33a921332",
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      "9e04bfb98d9e44b39f630f1e2badaef6",
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      "e60c6c77158143a6968a53dfc381325b",
      "2968e8540447476c9318158c39fa311a",
      "45f8b3a4620941c382543dbb09301252",
      "7e83bc1aa6264f4382d7d9cf5df56aa5",
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      "3cfd95948c9c4c70a0f7f7a2cc898332",
      "193aced1b2ac4d3194eec2da0680c7bc",
      "89ba933a9ca24eb3a1b13e281ca0a87e",
      "83e85675ac02482595c81ef8d8afda05",
      "4eb1f32b1f8f4802936457c6cfafeb13",
      "fcdf6806a3064a5d958b8f910e2ac7b5"
     ]
    },
    "id": "sLt-VLbWESJ_",
    "outputId": "c9185a1a-f5ae-4173-9bcd-2929b3e58014"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "de05a5d909ef44a78b90527c9b21ed5a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/746 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a6e3aad196c439690e8d2eef6e6f4dd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "96b45f6975d2493cacde2b9cd06993cf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25329c21ee9142759e531b3fbe4bf8bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)in/added_tokens.json:   0%|          | 0.00/21.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2968e8540447476c9318158c39fa311a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/435 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
    "tokenizer.pad_token = tokenizer.eos_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Fv-i1tk9qli_"
   },
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "4654f420216d403ba1bb94ad8e133fa7",
      "cb25719a1e0544d38659b281e0edb7fc",
      "a7cb76ec618147869dc927569ef017a6",
      "67095292b12c4d379687aa7ec15650c7",
      "438db9ceb4fa44b088d2cd75a9ba29b3",
      "0e905184dd3d4f1e9da6acc9d8816e9b",
      "3dad8eee6b4247508c25fe5cd0258687",
      "edb1d693495c4d7e87d7cc8d4f7e4ef7",
      "d2e394be23ad404d9f24f7a4c5699488",
      "d5fe82c61a1147c3ac25d91d7fe3dca1",
      "a49b002575e24621ad9e3e16d6c42df2"
     ]
    },
    "id": "Z_C4DXp2D9R7",
    "outputId": "72c63180-fd24-4c62-a592-7db872c18c9f"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4654f420216d403ba1bb94ad8e133fa7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "        model_name,\n",
    "        quantization_config=bnb_config,\n",
    "        device_map=device_map,\n",
    "    )\n",
    "model.config.pretraining_tp = 1\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EuiNhTp9EKRZ"
   },
   "outputs": [],
   "source": [
    "LORA_ALPHA = 16\n",
    "LORA_DROPOUT = 0.2\n",
    "LORA_R = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "p0nZjGVyEK5D"
   },
   "outputs": [],
   "source": [
    "peft_config = LoraConfig(\n",
    "        lora_alpha= LORA_ALPHA,\n",
    "        lora_dropout= LORA_DROPOUT,\n",
    "        r= LORA_R,\n",
    "        bias=\"none\",\n",
    "        task_type=\"CAUSAL_LM\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "S0V-LrniEN_k"
   },
   "outputs": [],
   "source": [
    "LEARNING_RATE = 1e-4\n",
    "NUM_EPOCHS = 10\n",
    "BATCH_SIZE = 16\n",
    "WEIGHT_DECAY = 0.001\n",
    "MAX_GRAD_NORM = 0.3\n",
    "gradient_accumulation_steps = 16\n",
    "STEPS = 1\n",
    "OPTIM = \"adam\"\n",
    "MAX_STEPS = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xGUAkSrxszWX"
   },
   "outputs": [],
   "source": [
    "OUTPUT_DIR = \"./results\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NJpgvX8is2RS"
   },
   "outputs": [],
   "source": [
    "training_args = TrainingArguments(\n",
    "    output_dir= OUTPUT_DIR,\n",
    "    per_device_train_batch_size=BATCH_SIZE,\n",
    "    gradient_accumulation_steps= gradient_accumulation_steps,\n",
    "    learning_rate= LEARNING_RATE,\n",
    "    logging_steps= STEPS,\n",
    "    num_train_epochs= NUM_EPOCHS,\n",
    "    max_steps= MAX_STEPS,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Bon_dYAQs4Qi"
   },
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "3b73e451fb874f71b2cd75150401931a",
      "5657a1744de743f98570e3745b465c63",
      "df66a0954c244d9898f30f9af0a8e075",
      "903bc6588ffb41c1aaba454496c82e31",
      "02f0c610884d4941bb284d6cd0a42c39",
      "73b3e5ff8b6744148f64b8c4c34402ae",
      "87b8c60f2f7d4289ae1874bfd33fb2e1",
      "de26b0b7065a4d30b5cb9ab82ab2a301",
      "06d0ff3be29948f1994091cc01fab506",
      "4f7324d32313447baa5bcdbff7dbc21a",
      "f95921c84d3240ed97caeefc8ed91092"
     ]
    },
    "id": "4LsS94z3s971",
    "outputId": "503abb0d-1056-45ef-9707-11f7f5dce444"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3b73e451fb874f71b2cd75150401931a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/16412 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    train_dataset=data,\n",
    "    peft_config=peft_config,\n",
    "    dataset_text_field=\"question\",\n",
    "    max_seq_length=500,\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_args,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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     "height": 426
    },
    "id": "HjwnBhx3tJMT",
    "outputId": "11c795c4-e829-4b92-f889-4a058b324c77"
   },
   "outputs": [
    {
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       "    <div>\n",
       "      \n",
       "      <progress value='10' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [10/10 14:05, Epoch 0/1]\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>1</td>\n",
       "      <td>4.001800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.930400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.021600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.873800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3.849200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3.808600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>3.831600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>3.814700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>3.741900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>3.919100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
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      "text/plain": [
       "TrainOutput(global_step=10, training_loss=3.879258561134338, metrics={'train_runtime': 939.7707, 'train_samples_per_second': 2.724, 'train_steps_per_second': 0.011, 'total_flos': 1401121747107840.0, 'train_loss': 3.879258561134338, 'epoch': 0.16})"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PlyhlS9kuAs7"
   },
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8Hi-rvmtt-li",
    "outputId": "eb983250-b12a-40da-e0a0-47692963e518"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
      "pip install xformers.\n"
     ]
    }
   ],
   "source": [
    "pipe = pipeline(task=\"text-generation\",model=model,tokenizer=tokenizer,max_length=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZnA_9cKWuNKl"
   },
   "outputs": [],
   "source": [
    "prompt = \"What is Glaucoma?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5UurFTERuTeB"
   },
   "outputs": [],
   "source": [
    "template = f\"\"\"<s>[INST] <<SYS>>\n",
    "You are a helpful, respectful and honest Medical assistant.\n",
    "You always answer to the context and do not hallucinate. You only answer to the questions that are related to Medical and Healthcare.\n",
    "If a question does not make any sense,Just answer I don't know, please don't share false information.\n",
    "<</SYS>>\n",
    "{prompt} [/INST]\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VI57iaEXuYto"
   },
   "outputs": [],
   "source": [
    "result = pipe(template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 147
    },
    "id": "pAetnirfuZUK",
    "outputId": "043bf072-fd86-480d-dac1-187d9c695e5f"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "\"<s>[INST] <<SYS>>\\nYou are a helpful, respectful and honest Medical assistant. \\nYou always answer to the context and do not hallucinate. You only answer to the questions that are related to Medical and Healthcare. \\nIf a question does not make any sense,Just answer I don't know, please don't share false information.\\n<</SYS>>\\nWhat is Glaucoma? [/INST]\\nHello! I'm here to help you with any questions you may have about medical and healthcare. Glaucoma is a group of eye conditions that can damage the optic nerve, which carries visual information from the eye to the brain. It is often associated with increased pressure inside the eye, which can lead to vision loss and even blindness if left untreated.\\n\\nThere are several types of glaucoma, including:\\n\\n1. Open-angle glaucoma: This is the most common form of glaucoma, and it occurs when the iris and the trabecular meshwork in the eye are not functioning properly, allowing fluid to build up and increase pressure inside the eye.\\n2. Closed-angle glaucoma: This type of glaucoma occurs when the iris is blocked, preventing fluid from leaving the eye and causing a sudden increase in pressure.\\n3. Normal-tension glaucoma: This type of glaucoma occurs when there is damage to the optic nerve even though the pressure inside the eye is within the normal range.\\n\\nSymptoms of glaucoma can include:\\n\\n* Blurred vision\\n* Loss of peripheral vision\\n* Eye pain or discomfort\\n* Nausea and vomiting\\n* Seeing halos around lights\\n* Difficulty seeing at night\\n\\nIf left untreated, glaucoma can lead to permanent vision loss and even blindness. However, with early detection and proper treatment, it is possible to slow or stop the progression of the disease. Treatment options for glaucoma include medication, laser surgery, and traditional surgery.\\n\\nI hope this information helps! If you have any further questions, please don't hesitate to ask.\""
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = result[0]['generated_text']\n",
    "response"
   ]
  }
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