{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5f4fff95",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-14T19:35:42.432381Z",
     "iopub.status.busy": "2024-04-14T19:35:42.431754Z",
     "iopub.status.idle": "2024-04-14T19:35:42.716363Z",
     "shell.execute_reply": "2024-04-14T19:35:42.715482Z"
    },
    "papermill": {
     "duration": 0.300523,
     "end_time": "2024-04-14T19:35:42.724182",
     "exception": false,
     "start_time": "2024-04-14T19:35:42.423659",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f2bb1db09b04b71950589dcecd7d0a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c8177ac6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-14T19:35:42.738841Z",
     "iopub.status.busy": "2024-04-14T19:35:42.738588Z",
     "iopub.status.idle": "2024-04-14T19:35:55.984612Z",
     "shell.execute_reply": "2024-04-14T19:35:55.983548Z"
    },
    "papermill": {
     "duration": 13.255837,
     "end_time": "2024-04-14T19:35:55.986856",
     "exception": false,
     "start_time": "2024-04-14T19:35:42.731019",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install -q datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c6fab6f2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-14T19:35:56.002049Z",
     "iopub.status.busy": "2024-04-14T19:35:56.001503Z",
     "iopub.status.idle": "2024-04-14T19:36:21.709829Z",
     "shell.execute_reply": "2024-04-14T19:36:21.708455Z"
    },
    "papermill": {
     "duration": 25.718232,
     "end_time": "2024-04-14T19:36:21.711968",
     "exception": false,
     "start_time": "2024-04-14T19:35:55.993736",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-14 19:36:03.642791: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-04-14 19:36:03.642886: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-04-14 19:36:03.768248: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c58e1aea7b6c48078e0adf3e1ed12ddf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/124 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading data: 100%|██████████| 2.06M/2.06M [00:00<00:00, 7.60MB/s]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "28603cdbe376473a9fddbaba15a9e233",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0246d290de934d708883f4af204b7d0b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "26ef1a4fe7d342afb77514ce9278f93d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6c5533b2038049f6a32eb2d0ccbbb29a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "835ccf8b47f24d5094be4a885fce907b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9e966923afb840bbab7845865918093f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "387f25c34faf49aeaada2b9490766cc1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted category: LABEL_1\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "import re\n",
    "from transformers import pipeline\n",
    "\n",
    "# Load the dataset\n",
    "dataset = load_dataset('neelblabla/enron_labeled_email-llama2-7b_finetuning')\n",
    "\n",
    "# Shuffle the dataset and slice it\n",
    "dataset = dataset['train'].shuffle(seed=42).select(range(1000))\n",
    "\n",
    "# Define a function to transform the data\n",
    "def transform_conversation(example):\n",
    "    conversation_text = example.get('prompts', '')  # Get conversation text, default to empty string if 'prompts' key is missing\n",
    "    \n",
    "    # Extract conversation text and category\n",
    "    inst_match = re.search(r'<s>\\[INST\\](.*?)</INST>', conversation_text, re.DOTALL)\n",
    "    inst_text = inst_match.group(1).strip() if inst_match else ''\n",
    "    \n",
    "    category_match = re.search(r'Category:(.*?)$', conversation_text, re.DOTALL)\n",
    "    category_text = category_match.group(1).strip() if category_match else ''\n",
    "\n",
    "    # Apply the new template\n",
    "    reformatted_text = f'<s>[INST] {inst_text} [/INST] {category_text} </s>'\n",
    "\n",
    "    return {'text': reformatted_text}\n",
    "\n",
    "# Apply the transformation\n",
    "transformed_dataset = dataset.map(transform_conversation)\n",
    "\n",
    "# Define the prompt\n",
    "prompt = \"I am sharing an email body with you. Based on the text in the body, you need to classify the email in one of the following eight categories: 'Company Business, Strategy, etc.'; 'Purely Personal'; 'Personal but in professional context (e.g., it was good working with you)'; 'Logistic Arrangements (meeting scheduling, technical support, etc)'; 'Employment arrangements (job seeking, hiring, recommendations, etc)'; 'Document editing/checking (collaboration)'; 'Empty message (due to missing attachment)'; 'Empty message'.\"\n",
    "\n",
    "# Define the text\n",
    "text = \"On this same subject of Chairman Wood I am told that he met with Rep. Doug Ose (energy subcommittee chairman of the Government Reform Committee) and Ose's energy advisory board on Wednesday of this week and said similar things. He made a big deal out of a Big Mac analogy -- saying that whether you ordered one in Portland Oregon or Portland Maine the product is the same. With RTOs he supposedly said this meant if the rules are standardized the number of RTOs becomes less important. Those in the room including EPSA staff came away with the impression that Wood is backing away from only 4 RTOs based on this and other comments. Ditto on a 12/15/01 deadline. On the positive side he did say that he had been consulted on the Administration's comments on the Bingaman bill and agreed that legislation without RTO language or bundled/unbundled is OK. He understands the high risk that Congress would go the wrong way on these issues.\"\n",
    "\n",
    "# Combine prompt and text\n",
    "combined_text = f\"{prompt} {text}\"\n",
    "\n",
    "# Load the text classification pipeline\n",
    "classifier = pipeline(\"text-classification\", model=\"distilbert-base-uncased\", tokenizer=\"distilbert-base-uncased\")\n",
    "\n",
    "# Classify the text\n",
    "classification_result = classifier(combined_text)\n",
    "\n",
    "# Print the predicted category\n",
    "print(\"Predicted category:\", classification_result[0]['label'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "74a6c5c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-14T19:36:21.730871Z",
     "iopub.status.busy": "2024-04-14T19:36:21.730360Z",
     "iopub.status.idle": "2024-04-14T19:36:23.824487Z",
     "shell.execute_reply": "2024-04-14T19:36:23.823132Z"
    },
    "papermill": {
     "duration": 2.105423,
     "end_time": "2024-04-14T19:36:23.826307",
     "exception": true,
     "start_time": "2024-04-14T19:36:21.720884",
     "status": "failed"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "HfHubHTTPError",
     "evalue": "401 Client Error: Unauthorized for url: https://huggingface.co/api/repos/create (Request ID: Root=1-661c3035-587b1b462bc0238143989f43;e381a574-914b-440c-aee4-1c098a674757)\n\nInvalid username or password.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py:304\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    303\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 304\u001b[0m     \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    305\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/requests/models.py:1021\u001b[0m, in \u001b[0;36mResponse.raise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1020\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m http_error_msg:\n\u001b[0;32m-> 1021\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(http_error_msg, response\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n",
      "\u001b[0;31mHTTPError\u001b[0m: 401 Client Error: Unauthorized for url: https://huggingface.co/api/repos/create",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mHfHubHTTPError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtransformed_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpush_to_hub\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mMSA-llama7b\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:5391\u001b[0m, in \u001b[0;36mDataset.push_to_hub\u001b[0;34m(self, repo_id, config_name, set_default, split, data_dir, commit_message, commit_description, private, token, revision, branch, create_pr, max_shard_size, num_shards, embed_external_files)\u001b[0m\n\u001b[1;32m   5387\u001b[0m     revision \u001b[38;5;241m=\u001b[39m branch\n\u001b[1;32m   5389\u001b[0m api \u001b[38;5;241m=\u001b[39m HfApi(endpoint\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mHF_ENDPOINT, token\u001b[38;5;241m=\u001b[39mtoken)\n\u001b[0;32m-> 5391\u001b[0m repo_url \u001b[38;5;241m=\u001b[39m \u001b[43mapi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_repo\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   5392\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5393\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5394\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdataset\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5395\u001b[0m \u001b[43m    \u001b[49m\u001b[43mprivate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprivate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5396\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   5397\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   5398\u001b[0m repo_id \u001b[38;5;241m=\u001b[39m repo_url\u001b[38;5;241m.\u001b[39mrepo_id\n\u001b[1;32m   5400\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m revision \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:119\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m    117\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 119\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/huggingface_hub/hf_api.py:3360\u001b[0m, in \u001b[0;36mHfApi.create_repo\u001b[0;34m(self, repo_id, token, private, repo_type, exist_ok, space_sdk, space_hardware, space_storage, space_sleep_time, space_secrets, space_variables)\u001b[0m\n\u001b[1;32m   3357\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m   3359\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3360\u001b[0m     \u001b[43mhf_raise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3361\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m   3362\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m exist_ok \u001b[38;5;129;01mand\u001b[39;00m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m409\u001b[39m:\n\u001b[1;32m   3363\u001b[0m         \u001b[38;5;66;03m# Repo already exists and `exist_ok=True`\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py:371\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    367\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HfHubHTTPError(message, response\u001b[38;5;241m=\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    369\u001b[0m \u001b[38;5;66;03m# Convert `HTTPError` into a `HfHubHTTPError` to display request information\u001b[39;00m\n\u001b[1;32m    370\u001b[0m \u001b[38;5;66;03m# as well (request id and/or server error message)\u001b[39;00m\n\u001b[0;32m--> 371\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m HfHubHTTPError(\u001b[38;5;28mstr\u001b[39m(e), response\u001b[38;5;241m=\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
      "\u001b[0;31mHfHubHTTPError\u001b[0m: 401 Client Error: Unauthorized for url: https://huggingface.co/api/repos/create (Request ID: Root=1-661c3035-587b1b462bc0238143989f43;e381a574-914b-440c-aee4-1c098a674757)\n\nInvalid username or password."
     ]
    }
   ],
   "source": [
    "transformed_dataset.push_to_hub(\"MSA-llama7b\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3d6c87d",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
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    "tags": []
   },
   "source": [
    "# # Install All the Required Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f72c4cf4",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.33.1 trl==0.4.7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0b81744",
   "metadata": {
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    "tags": []
   },
   "source": [
    "# # Import All the Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0838a00",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "from datasets import load_dataset\n",
    "from transformers import (\n",
    "    AutoModelForCausalLM,\n",
    "    AutoTokenizer,\n",
    "    BitsAndBytesConfig,\n",
    "    HfArgumentParser,\n",
    "    TrainingArguments,\n",
    "    pipeline,\n",
    "    logging,\n",
    ")\n",
    "from peft import LoraConfig, PeftModel\n",
    "from trl import SFTTrainer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24658531",
   "metadata": {
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     "duration": null,
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    "tags": []
   },
   "source": [
    "# # Load a llama-2-7b-chat-hf model and Train it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa31904b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-14T18:26:35.397327Z",
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   },
   "outputs": [],
   "source": [
    "# The model that you want to train from the Hugging Face hub\n",
    "model_name = \"NousResearch/Llama-2-7b-chat-hf\"\n",
    "\n",
    "# The instruction dataset to use\n",
    "dataset_name = \"Adignite/MSA-llama7b\"\n",
    "\n",
    "# Fine-tuned model name\n",
    "new_model = \"Llama-2-7b-chat-MailSense-finetune\"\n",
    "\n",
    "################################################################################\n",
    "# QLoRA parameters\n",
    "################################################################################\n",
    "\n",
    "# LoRA attention dimension\n",
    "lora_r = 64\n",
    "\n",
    "# Alpha parameter for LoRA scaling\n",
    "lora_alpha = 16\n",
    "\n",
    "# Dropout probability for LoRA layers\n",
    "lora_dropout = 0.1\n",
    "\n",
    "################################################################################\n",
    "# bitsandbytes parameters\n",
    "################################################################################\n",
    "\n",
    "# Activate 4-bit precision base model loading\n",
    "use_4bit = True\n",
    "\n",
    "# Compute dtype for 4-bit base models\n",
    "bnb_4bit_compute_dtype = \"float16\"\n",
    "\n",
    "# Quantization type (fp4 or nf4)\n",
    "bnb_4bit_quant_type = \"nf4\"\n",
    "\n",
    "# Activate nested quantization for 4-bit base models (double quantization)\n",
    "use_nested_quant = False\n",
    "\n",
    "################################################################################\n",
    "# TrainingArguments parameters\n",
    "################################################################################\n",
    "\n",
    "# Output directory where the model predictions and checkpoints will be stored\n",
    "output_dir = \"./results\"\n",
    "\n",
    "# Number of training epochs\n",
    "num_train_epochs = 1\n",
    "\n",
    "# Enable fp16/bf16 training (set bf16 to True with an A100)\n",
    "fp16 = False\n",
    "bf16 = False\n",
    "\n",
    "# Batch size per GPU for training\n",
    "per_device_train_batch_size = 4\n",
    "\n",
    "# Batch size per GPU for evaluation\n",
    "per_device_eval_batch_size = 4\n",
    "\n",
    "# Number of update steps to accumulate the gradients for\n",
    "gradient_accumulation_steps = 1\n",
    "\n",
    "# Enable gradient checkpointing\n",
    "gradient_checkpointing = True\n",
    "\n",
    "# Maximum gradient normal (gradient clipping)\n",
    "max_grad_norm = 0.3\n",
    "\n",
    "# Initial learning rate (AdamW optimizer)\n",
    "learning_rate = 2e-4\n",
    "\n",
    "# Weight decay to apply to all layers except bias/LayerNorm weights\n",
    "weight_decay = 0.001\n",
    "\n",
    "# Optimizer to use\n",
    "optim = \"paged_adamw_32bit\"\n",
    "\n",
    "# Learning rate schedule\n",
    "lr_scheduler_type = \"cosine\"\n",
    "\n",
    "# Number of training steps (overrides num_train_epochs)\n",
    "max_steps = -1\n",
    "\n",
    "# Ratio of steps for a linear warmup (from 0 to learning rate)\n",
    "warmup_ratio = 0.03\n",
    "\n",
    "# Group sequences into batches with same length\n",
    "# Saves memory and speeds up training considerably\n",
    "group_by_length = True\n",
    "\n",
    "# Save checkpoint every X updates steps\n",
    "save_steps = 0\n",
    "\n",
    "# Log every X updates steps\n",
    "logging_steps = 25\n",
    "\n",
    "################################################################################\n",
    "# SFT parameters\n",
    "################################################################################\n",
    "\n",
    "# Maximum sequence length to use\n",
    "max_seq_length = None\n",
    "\n",
    "# Pack multiple short examples in the same input sequence to increase efficiency\n",
    "packing = False\n",
    "\n",
    "# Load the entire model on the GPU 0\n",
    "device_map = {\"\": 0}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c1b687",
   "metadata": {
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    "tags": []
   },
   "source": [
    "# # Load everything and start the fine-tuning process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c28a8c3",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "# Load dataset\n",
    "dataset = load_dataset(dataset_name, split=\"train\")\n",
    "\n",
    "# Load tokenizer and model with QLoRA configuration\n",
    "compute_dtype = getattr(torch, bnb_4bit_compute_dtype)\n",
    "\n",
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=use_4bit,\n",
    "    bnb_4bit_quant_type=bnb_4bit_quant_type,\n",
    "    bnb_4bit_compute_dtype=compute_dtype,\n",
    "    bnb_4bit_use_double_quant=use_nested_quant,\n",
    ")\n",
    "\n",
    "# Check GPU compatibility with bfloat16\n",
    "if compute_dtype == torch.float16 and use_4bit:\n",
    "    major, _ = torch.cuda.get_device_capability()\n",
    "    if major >= 8:\n",
    "        print(\"=\" * 80)\n",
    "        print(\"Your GPU supports bfloat16: accelerate training with bf16=True\")\n",
    "        print(\"=\" * 80)\n",
    "\n",
    "# Load base model\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_name,\n",
    "    quantization_config=bnb_config,\n",
    "    device_map=device_map\n",
    ")\n",
    "model.config.use_cache = False\n",
    "model.config.pretraining_tp = 1\n",
    "\n",
    "# Load LLaMA tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.padding_side = \"right\"  # Fix weird overflow issue with fp16 training\n",
    "\n",
    "# Load LoRA configuration\n",
    "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",
    ")\n",
    "\n",
    "# Set training parameters\n",
    "training_arguments = TrainingArguments(\n",
    "    output_dir=output_dir,\n",
    "    num_train_epochs=num_train_epochs,\n",
    "    per_device_train_batch_size=per_device_train_batch_size,\n",
    "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
    "    optim=optim,\n",
    "    save_steps=save_steps,\n",
    "    logging_steps=logging_steps,\n",
    "    learning_rate=learning_rate,\n",
    "    weight_decay=weight_decay,\n",
    "    fp16=fp16,\n",
    "    bf16=bf16,\n",
    "    max_grad_norm=max_grad_norm,\n",
    "    max_steps=max_steps,\n",
    "    warmup_ratio=warmup_ratio,\n",
    "    group_by_length=group_by_length,\n",
    "    lr_scheduler_type=lr_scheduler_type,\n",
    "    report_to=\"tensorboard\"\n",
    ")\n",
    "\n",
    "# Set supervised fine-tuning parameters\n",
    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    train_dataset=dataset,\n",
    "    peft_config=peft_config,\n",
    "    dataset_text_field=\"text\",\n",
    "    max_seq_length=max_seq_length,\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_arguments,\n",
    "    packing=packing,\n",
    ")\n",
    "\n",
    "# Train model\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a61348c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-14T18:34:30.798159Z",
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   },
   "outputs": [],
   "source": [
    "# Save trained model\n",
    "trainer.model.save_pretrained(new_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "018bdbec",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
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    "tags": []
   },
   "source": [
    "Check the plots on tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cd1c508",
   "metadata": {
    "collapsed": true,
    "execution": {
     "iopub.execute_input": "2024-04-14T18:34:37.230659Z",
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    },
    "jupyter": {
     "outputs_hidden": true
    },
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   "outputs": [],
   "source": [
    "%load_ext tensorboard\n",
    "%tensorboard --logdir results/runs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31ab0203",
   "metadata": {
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    "tags": []
   },
   "source": [
    "# # Use the text generation pipeline to ask questions\n",
    "# # # Enter your prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fae90ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-14T18:50:58.421836Z",
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   },
   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "import re\n",
    "\n",
    "# Run text generation pipeline with our next model\n",
    "prompt = \"I am sharing an email body with you. Based on the text in the body, you need to classify the email in one of the following eight categories: 'Company Business, Strategy, etc.'; 'Purely Personal'; 'Personal but in professional context (e.g., it was good working with you)'; 'Logistic Arrangements (meeting scheduling, technical support, etc)'; 'Employment arrangements (job seeking, hiring, recommendations, etc)'; 'Document editing/checking (collaboration)'; 'Empty message (due to missing attachment)'; 'Empty message'.\"\n",
    "\n",
    "# Load the text generation pipeline with the fine-tuned model and tokenizer\n",
    "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer)\n",
    "\n",
    "# Generate text based on the provided prompt\n",
    "generated_text = pipe(f\"<s>[INST] {prompt} [/INST]\")[0]['generated_text']\n",
    "\n",
    "# Print the generated text\n",
    "print(\"Generated Text:\", generated_text)\n",
    "\n",
    "# Now classify the generated text into one of the categories\n",
    "# You can use any classification model or function that you have\n",
    "# Here's a simple example using regex pattern matching\n",
    "categories = [\n",
    "    \"Company Business, Strategy, etc.\",\n",
    "    \"Purely Personal\",\n",
    "    \"Personal but in professional context (e.g., it was good working with you)\",\n",
    "    \"Logistic Arrangements (meeting scheduling, technical support, etc)\",\n",
    "    \"Employment arrangements (job seeking, hiring, recommendations, etc)\",\n",
    "    \"Document editing/checking (collaboration)\",\n",
    "    \"Empty message (due to missing attachment)\",\n",
    "    \"Empty message\"\n",
    "]\n",
    "\n",
    "# Pattern matching to classify the generated text\n",
    "classified_category = None\n",
    "for category in categories:\n",
    "    if re.search(category, generated_text):\n",
    "        classified_category = category\n",
    "        break\n",
    "\n",
    "# Print the classified category\n",
    "print(\"Classified Category:\", classified_category)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d012773",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-14T18:51:07.921815Z",
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     "shell.execute_reply": "2024-04-14T18:51:08.821543Z",
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    },
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   "outputs": [],
   "source": [
    "# Empty VRAM\n",
    "del model\n",
    "del pipe\n",
    "del trainer\n",
    "import gc\n",
    "gc.collect()\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e568b3a2",
   "metadata": {
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    },
    "tags": []
   },
   "source": [
    "# # Store New Llama2 Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d31628b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-14T18:51:30.486392Z",
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   },
   "outputs": [],
   "source": [
    "# Reload model in FP16 and merge it with LoRA weights\n",
    "base_model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_name,\n",
    "    low_cpu_mem_usage=True,\n",
    "    return_dict=True,\n",
    "    torch_dtype=torch.float16,\n",
    "    device_map=device_map,\n",
    ")\n",
    "model = PeftModel.from_pretrained(base_model, new_model)\n",
    "model = model.merge_and_unload()\n",
    "\n",
    "# Reload tokenizer to save it\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.padding_side = \"right\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f38f2edc",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
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    },
    "tags": []
   },
   "source": [
    "#  Push Model to Hugging Face Hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2803120",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-14T18:53:29.233630Z",
     "iopub.status.busy": "2024-04-14T18:53:29.233217Z",
     "iopub.status.idle": "2024-04-14T18:53:29.238223Z",
     "shell.execute_reply": "2024-04-14T18:53:29.237254Z",
     "shell.execute_reply.started": "2024-04-14T18:53:29.233595Z"
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     "duration": null,
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     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import locale\n",
    "locale.getpreferredencoding = lambda: \"UTF-8\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5b535a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-14T18:53:37.375331Z",
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    },
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   },
   "outputs": [],
   "source": [
    "notebook_login()"
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