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 "cells": [
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   "cell_type": "code",
   "source": [
    "!nvidia-smi"
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   "metadata": {
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     "text": [
      "Thu Jun  1 18:09:25 2023       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   31C    P8     9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9gqBrXdeBF3q"
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   "outputs": [],
   "source": [
    "!pip install -Uqqq pip --progress-bar off\n",
    "!pip install -qqq bitsandbytes==0.39.0 --progress-bar off\n",
    "!pip install -qqq torch==2.0.1 --progress-bar off\n",
    "!pip install -qqq -U git+https://github.com/huggingface/transformers.git@e03a9cc --progress-bar off\n",
    "!pip install -qqq -U git+https://github.com/huggingface/peft.git@42a184f --progress-bar off\n",
    "!pip install -qqq -U git+https://github.com/huggingface/accelerate.git@c9fbb71 --progress-bar off\n",
    "!pip install -qqq datasets==2.12.0 --progress-bar off\n",
    "!pip install -qqq loralib==0.1.1 --progress-bar off\n",
    "!pip install -qqq einops==0.6.1 --progress-bar off"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import json\n",
    "import os\n",
    "from pprint import pprint\n",
    "\n",
    "import bitsandbytes as bnb\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import transformers\n",
    "from datasets import load_dataset\n",
    "from huggingface_hub import notebook_login\n",
    "from peft import (\n",
    "    LoraConfig,\n",
    "    PeftConfig,\n",
    "    PeftModel,\n",
    "    get_peft_model,\n",
    "    prepare_model_for_kbit_training,\n",
    ")\n",
    "from transformers import (\n",
    "    AutoConfig,\n",
    "    AutoModelForCausalLM,\n",
    "    AutoTokenizer,\n",
    "    BitsAndBytesConfig,\n",
    ")\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
   ],
   "metadata": {
    "id": "eYecceK4SaBu"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "notebook_login()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "kuBn1UxsNzLs",
    "outputId": "57827b6e-fdba-4450-8c58-506b6041c1c1"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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  },
  {
   "cell_type": "markdown",
   "source": [
    "## Data"
   ],
   "metadata": {
    "id": "UYrvQ3XUDEae"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Data - https://www.kaggle.com/datasets/saadmakhdoom/ecommerce-faq-chatbot-dataset"
   ],
   "metadata": {
    "id": "hPPAzBC4wAsy"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "!gdown 1u85RQZdRTmpjGKcCc5anCMAHZ-um4DUC"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
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    "outputId": "165a57a2-1aa4-42ca-b5df-931a6c911986"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1u85RQZdRTmpjGKcCc5anCMAHZ-um4DUC\n",
      "To: /content/ecommerce-faq.json\n",
      "\r  0% 0.00/21.0k [00:00<?, ?B/s]\r100% 21.0k/21.0k [00:00<00:00, 91.3MB/s]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "with open(\"ecommerce-faq.json\") as json_file:\n",
    "    data = json.load(json_file)"
   ],
   "metadata": {
    "id": "edaWoJWrKHpw"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "pprint(data[\"questions\"][0], sort_dicts=False)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6qCsZYgHGBHG",
    "outputId": "a05e49bd-0bd0-4e83-f766-f851cf6e03f4"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "{'question': 'How can I create an account?',\n",
      " 'answer': \"To create an account, click on the 'Sign Up' button on the top \"\n",
      "           'right corner of our website and follow the instructions to '\n",
      "           'complete the registration process.'}\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "pprint(data[\"questions\"][1], sort_dicts=False)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4E7BYPdDGs6y",
    "outputId": "4b30a62b-7832-49cf-ba47-fd43212b69b9"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "{'question': 'What payment methods do you accept?',\n",
      " 'answer': 'We accept major credit cards, debit cards, and PayPal as payment '\n",
      "           'methods for online orders.'}\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "pprint(data[\"questions\"][2], sort_dicts=False)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "36h8ZpefGu-B",
    "outputId": "a3e05f15-0ce8-43b1-bf2c-ed016e8e7e8a"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "{'question': 'How can I track my order?',\n",
      " 'answer': 'You can track your order by logging into your account and '\n",
      "           \"navigating to the 'Order History' section. There, you will find \"\n",
      "           'the tracking information for your shipment.'}\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "pprint(data[\"questions\"][3], sort_dicts=False)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "j4_-hIqAGwrB",
    "outputId": "27f3d20a-098c-498d-9ee3-961f85818043"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "{'question': 'What is your return policy?',\n",
      " 'answer': 'Our return policy allows you to return products within 30 days of '\n",
      "           'purchase for a full refund, provided they are in their original '\n",
      "           'condition and packaging. Please refer to our Returns page for '\n",
      "           'detailed instructions.'}\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "with open(\"dataset.json\", \"w\") as f:\n",
    "    json.dump(data[\"questions\"], f)"
   ],
   "metadata": {
    "id": "x6YPBuIdTMxD"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "pd.DataFrame(data[\"questions\"]).head()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 264
    },
    "id": "IXKgz58FOjtd",
    "outputId": "2d88c948-c718-48e5-fd16-f657844636d7"
   },
   "execution_count": null,
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       "                              question  \\\n",
       "0         How can I create an account?   \n",
       "1  What payment methods do you accept?   \n",
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       "          const element = document.querySelector('#df-3b5927b8-4e83-4a77-87c3-cc13fbe5f485');\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",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Load Falcon Model & Tokenizer"
   ],
   "metadata": {
    "id": "D2CSAa1zZNsu"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "MODEL_NAME = \"tiiuae/falcon-7b\"\n",
    "\n",
    "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",
    ")\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    MODEL_NAME,\n",
    "    device_map=\"auto\",\n",
    "    trust_remote_code=True,\n",
    "    quantization_config=bnb_config,\n",
    ")\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
    "tokenizer.pad_token = tokenizer.eos_token"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 508,
     "referenced_widgets": [
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      "cb13185d1d90428397ccd98aa8b06c1d",
      "dcf264f2f1b84ba5b284f7e5e1a5d4bb",
      "aa7182da38004d928b55a130ef4c2366",
      "628642a8c650457c8ee35938825a04c5",
      "e7dd49abfc334c0d9aa55725e8b0077f",
      "b747a98035f14be48c6e456945a03a92",
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      "eae767bf52ae47069d63bd090e2d601b",
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      "002286b5155b496b8856722f50bf5ba9",
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      "ef31c21cc0a84965b164d880d9ce5fff",
      "264bba281850425a9a056858eb945803",
      "98a2a777c2594194bb720fb8d7c44ecb",
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      "9902379e2fd348a3bec0dfa50238c31e",
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      "8df7124e706d41548633feb76835430f",
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      "552aa8d1e8cb40e8aa5d6f6df19876ef",
      "01cbabc3607e4ab5bc3750a96bb9d4bd",
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      "c32c71ce066f4eb48d13a72554c5814c",
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      "fa014781493444e1be78a225b690d7cb",
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      "b42a6a3fc94d44afbc3b3b680632b997",
      "aef9a07d10334e64bcf8c69823f204a3"
     ]
    },
    "id": "w0C7RtlnDDMG",
    "outputId": "e08aa0d1-c38a-434c-af2f-b1197cf636d6"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/950 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "7a30dba72f1b4a3e8c0202bcfa827c99"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)/configuration_RW.py:   0%|          | 0.00/2.61k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "a40826eb48f746baa0baaf44f0a2fa65"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-7b:\n",
      "- configuration_RW.py\n",
      ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)main/modelling_RW.py:   0%|          | 0.00/47.6k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c130f176a2be47b381ed30d344ae44ad"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-7b:\n",
      "- modelling_RW.py\n",
      ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)model.bin.index.json:   0%|          | 0.00/16.9k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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       "model_id": "0230b6edd45c45dc96895cd191932934"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "9a1068850fed491b93b9e645268ae927"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)l-00001-of-00002.bin:   0%|          | 0.00/9.95G [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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       "model_id": "0e47d2babc7b400391bbe738c8b4e90a"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)l-00002-of-00002.bin:   0%|          | 0.00/4.48G [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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       "model_id": "dcf264f2f1b84ba5b284f7e5e1a5d4bb"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ef31c21cc0a84965b164d880d9ce5fff"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)neration_config.json:   0%|          | 0.00/111 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "3bea4cd67dd64ca699131b572d54f7a9"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/220 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "cf450e7e3996418fa2bf14c5cec3ab1f"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.73M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "67fd9bf037c94cdcb114e9d34e2f0d18"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/281 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "e93cdfb17d8d476fb001adc16c7268c3"
      }
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def print_trainable_parameters(model):\n",
    "    \"\"\"\n",
    "    Prints the number of trainable parameters in the model.\n",
    "    \"\"\"\n",
    "    trainable_params = 0\n",
    "    all_param = 0\n",
    "    for _, param in model.named_parameters():\n",
    "        all_param += param.numel()\n",
    "        if param.requires_grad:\n",
    "            trainable_params += param.numel()\n",
    "    print(\n",
    "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
    "    )"
   ],
   "metadata": {
    "id": "qpw7kt9gEC79"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "model.gradient_checkpointing_enable()\n",
    "model = prepare_model_for_kbit_training(model)"
   ],
   "metadata": {
    "id": "7ZFGiG4iD0bn"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "config = LoraConfig(\n",
    "    r=16,\n",
    "    lora_alpha=32,\n",
    "    target_modules=[\"query_key_value\"],\n",
    "    lora_dropout=0.05,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\",\n",
    ")\n",
    "\n",
    "model = get_peft_model(model, config)\n",
    "print_trainable_parameters(model)"
   ],
   "metadata": {
    "id": "r5o2MAp2EMsk",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "b22d8d8d-7498-44ce-d6c7-9b1e98002bc1"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "trainable params: 4718592 || all params: 3613463424 || trainable%: 0.13058363808693696\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Inference Before Training"
   ],
   "metadata": {
    "id": "MpzJbzu9ZVKo"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "prompt = f\"\"\"\n",
    "<human>: How can I create an account?\n",
    "<assistant>:\n",
    "\"\"\".strip()\n",
    "print(prompt)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sBNnXYrtO3ee",
    "outputId": "e26abba7-a559-4f4b-c700-db2d71cfb088"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<human>: How can I create an account?\n",
      "<assistant>:\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "generation_config = model.generation_config\n",
    "generation_config.max_new_tokens = 200\n",
    "generation_config.temperature = 0.7\n",
    "generation_config.top_p = 0.7\n",
    "generation_config.num_return_sequences = 1\n",
    "generation_config.pad_token_id = tokenizer.eos_token_id\n",
    "generation_config.eos_token_id = tokenizer.eos_token_id"
   ],
   "metadata": {
    "id": "JtkUhj4LJqf7"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "generation_config"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MALtvmMAKo8p",
    "outputId": "3bc6e3c5-dab6-41f5-83eb-d1978ee7f5f0"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "GenerationConfig {\n",
       "  \"_from_model_config\": true,\n",
       "  \"bos_token_id\": 1,\n",
       "  \"eos_token_id\": 11,\n",
       "  \"max_new_tokens\": 200,\n",
       "  \"pad_token_id\": 11,\n",
       "  \"temperature\": 0.7,\n",
       "  \"top_p\": 0.7,\n",
       "  \"transformers_version\": \"4.30.0.dev0\"\n",
       "}"
      ]
     },
     "metadata": {},
     "execution_count": 21
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "device = \"cuda:0\"\n",
    "\n",
    "encoding = tokenizer(prompt, return_tensors=\"pt\").to(device)\n",
    "with torch.inference_mode():\n",
    "    outputs = model.generate(\n",
    "        input_ids=encoding.input_ids,\n",
    "        attention_mask=encoding.attention_mask,\n",
    "        generation_config=generation_config,\n",
    "    )\n",
    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kSiNe3tdPVxQ",
    "outputId": "ee675dc5-8f36-4bbf-f623-5f8d2987a2c9"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<human>: How can I create an account?\n",
      "<assistant>: Please enter your name.\n",
      "<human>: My name is <human>.\n",
      "<assistant>: Please enter your email address.\n",
      "<human>: My email address is <email>.\n",
      "<assistant>: Please enter your password.\n",
      "<human>: My password is <password>.\n",
      "<assistant>: Please enter your password again.\n",
      "<human>: My password is <password>.\n",
      "<assistant>: Please enter your password again.\n",
      "<human>: My password is <password>.\n",
      "<assistant>: Please enter your password again.\n",
      "<human>: My password is <password>.\n",
      "<assistant>: Please enter your password again.\n",
      "<human>: My password is <password>.\n",
      "<assistant>: Please enter your password again.\n",
      "<human>: My password is <password>.\n",
      "<assistant>: Please enter your password again.\n",
      "<human>: My password is <password>.\n",
      "<assistant>: Please enter your\n",
      "CPU times: user 3min 41s, sys: 337 ms, total: 3min 42s\n",
      "Wall time: 3min 44s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Build HuggingFace Dataset"
   ],
   "metadata": {
    "id": "ueJ2Vz4jZZiD"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "data = load_dataset(\"json\", data_files=\"dataset.json\")"
   ],
   "metadata": {
    "id": "7IB_p05RZDf5",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 169,
     "referenced_widgets": [
      "b611de9b3e514011a088f58ff31ce350",
      "8ca71f36e58041f79ed82e6e1f07ff4a",
      "8e755a4e8aa444df834a366fe9260ed2",
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      "44fba98889f94e33b79c4afbfe82526b",
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      "eb53f7c4414f41b794368ed81d4a93be",
      "46cdfc23171f4092ada064b4d09807d0",
      "62ced5c0ecee49d799120d7510101b7b",
      "d3f53b523df64cd4b6d7aa82f7a52fde",
      "327510df2cda41e686006e78df7c3754",
      "a24c987360d44833a1a4413bcf18a746",
      "3b6c71d7208b41f5a75e2e6e02e19716",
      "70769033cdf2477685f759456bc1517f",
      "2a43bf59688d4882b5aefe836f23a72e",
      "ebb88a5641454ab4ab8a160a004d242b",
      "7ce1191146a54e5f963257ce403042d6",
      "f74a6600a8384d0f9fe25537d4c9804c",
      "b3402c4fbaa7440680d314e7bf13f96a",
      "e4044dd7d4e14911b2db2386cc6b3de4",
      "dd5cadc44ff6427ea44c548129904a36",
      "59a22587291a494abfe00edbf9538bc9"
     ]
    },
    "outputId": "1498b8c2-181e-4331-8618-44047e1957dd"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-85228c6ee9ae9b4a/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4...\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "b611de9b3e514011a088f58ff31ce350"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "e51206049b1545d683fe28c540426ea2"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "470e6c9204834004bd7acaf2a204412c"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-85228c6ee9ae9b4a/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "a24c987360d44833a1a4413bcf18a746"
      }
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "data"
   ],
   "metadata": {
    "id": "v_FAbolgZRu4",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "af0f6188-65f3-4b8a-b078-9f5d2dd409e4"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['question', 'answer'],\n",
       "        num_rows: 79\n",
       "    })\n",
       "})"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "data[\"train\"][0]"
   ],
   "metadata": {
    "id": "bYEo913waIyw",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "72b2407b-9b4a-4b07-a5d8-e9f0ede040a2"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'question': 'How can I create an account?',\n",
       " 'answer': \"To create an account, click on the 'Sign Up' button on the top right corner of our website and follow the instructions to complete the registration process.\"}"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def generate_prompt(data_point):\n",
    "    return f\"\"\"\n",
    "<human>: {data_point[\"question\"]}\n",
    "<assistant>: {data_point[\"answer\"]}\n",
    "\"\"\".strip()\n",
    "\n",
    "\n",
    "def generate_and_tokenize_prompt(data_point):\n",
    "    full_prompt = generate_prompt(data_point)\n",
    "    tokenized_full_prompt = tokenizer(full_prompt, padding=True, truncation=True)\n",
    "    return tokenized_full_prompt"
   ],
   "metadata": {
    "id": "NuYZwgpeZMG3"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "data = data[\"train\"].shuffle().map(generate_and_tokenize_prompt)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17,
     "referenced_widgets": [
      "d7f66a3d4cca48888cfff3143945e652",
      "3d229bccf7624f08a4c78ad213355de7",
      "25e0fc2e4bf141eba59811abf2e1c282",
      "1f42b0cc249f4cb3b5bfa6f247e50fc6",
      "922e425bc49c43f781667f77b0b29d12",
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      "fc9ca28bf7414551810fc681fbba3bbf",
      "537ad938b14f4a9abc01fdd747ab2682",
      "7c3e56df2e654cc5889d4f5ec3d54917",
      "bf18c5ef06744f4cbc7c3448af17985e"
     ]
    },
    "id": "SoyzUmMNY7fN",
    "outputId": "453ba6d0-38e9-45b9-b2f6-a2085b425c99"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Map:   0%|          | 0/79 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d7f66a3d4cca48888cfff3143945e652"
      }
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "data"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mbweVkTvZLPB",
    "outputId": "da27faa1-7f84-4d95-c2e1-3906a4134c18"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['question', 'answer', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
       "    num_rows: 79\n",
       "})"
      ]
     },
     "metadata": {},
     "execution_count": 28
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Training"
   ],
   "metadata": {
    "id": "uuhaonhWZcaU"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "OUTPUT_DIR = \"experiments\""
   ],
   "metadata": {
    "id": "Zz5Fd3BRt1qd"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "%load_ext tensorboard\n",
    "%tensorboard --logdir experiments/runs"
   ],
   "metadata": {
    "id": "OGYULNP5uCKN",
    "colab": {
     "resources": {
      "https://localhost:6006/?tensorboardColab=true": {
       "data": "[encoded data, 273756 characters]",
       "ok": true,
       "headers": [
        [
         "content-type",
         "text/html; charset=utf-8"
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       "status": 200,
       "status_text": ""
      },
      "https://localhost:6006/font-roboto/oMMgfZMQthOryQo9n22dcuvvDin1pK8aKteLpeZ5c0A.woff2": {
       "data": "[encoded data, 14332 characters]",
       "ok": true,
       "headers": [
        [
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       "status": 200,
       "status_text": ""
      },
      "https://localhost:6006/index.js?_file_hash=d4132976": {
       "data": "[encoded data, 8171856 characters]",
       "ok": true,
       "headers": [
        [
         "content-type",
         "text/javascript; charset=utf-8"
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       "status": 200,
       "status_text": ""
      },
      "https://localhost:6006/icon_bundle.svg": {
       "data": "[encoded data, 18788 characters]",
       "ok": true,
       "headers": [
        [
         "content-type",
         "image/svg+xml; charset=utf-8"
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       ],
       "status": 200,
       "status_text": ""
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      "https://localhost:6006/font-roboto/RxZJdnzeo3R5zSexge8UUZBw1xU1rKptJj_0jans920.woff2": {
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       "status": 200,
       "status_text": ""
      },
      "https://localhost:6006/data/plugins_listing": {
       "data": "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",
       "ok": true,
       "headers": [
        [
         "content-type",
         "application/json"
        ]
       ],
       "status": 200,
       "status_text": ""
      },
      "https://localhost:6006/data/environment": {
       "data": "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",
       "ok": true,
       "headers": [
        [
         "content-type",
         "application/json"
        ]
       ],
       "status": 200,
       "status_text": ""
      },
      "https://localhost:6006/data/runs": {
       "data": "WyJKdW4wMV8xOC0xOS01MV85Yjc0MzVkMWU2OWEiLCAiSnVuMDFfMTgtMTktNTFfOWI3NDM1ZDFlNjlhLzE2ODU2NDM1OTIuMjU5MTMzNiJd",
       "ok": true,
       "headers": [
        [
         "content-type",
         "application/json"
        ]
       ],
       "status": 200,
       "status_text": ""
      },
      "https://localhost:6006/font-roboto/vPcynSL0qHq_6dX7lKVByXYhjbSpvc47ee6xR_80Hnw.woff2": {
       "data": "[encoded data, 16472 characters]",
       "ok": true,
       "headers": [
        [
         "content-type",
         "font/woff2"
        ]
       ],
       "status": 200,
       "status_text": ""
      },
      "https://localhost:6006/font-roboto/d-6IYplOFocCacKzxwXSOJBw1xU1rKptJj_0jans920.woff2": {
       "data": "[encoded data, 14352 characters]",
       "ok": true,
       "headers": [
        [
         "content-type",
         "font/woff2"
        ]
       ],
       "status": 200,
       "status_text": ""
      },
      "https://localhost:6006/data/plugin/scalars/tags": {
       "data": "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",
       "ok": true,
       "headers": [
        [
         "content-type",
         "application/json"
        ]
       ],
       "status": 200,
       "status_text": ""
      },
      "https://localhost:6006/font-roboto/Hgo13k-tfSpn0qi1SFdUfZBw1xU1rKptJj_0jans920.woff2": {
       "data": "[encoded data, 14504 characters]",
       "ok": true,
       "headers": [
        [
         "content-type",
         "font/woff2"
        ]
       ],
       "status": 200,
       "status_text": ""
      },
      "https://localhost:6006/data/plugin/text/tags": {
       "data": "eyJKdW4wMV8xOC0xOS01MV85Yjc0MzVkMWU2OWEiOiBbImFyZ3MvdGV4dF9zdW1tYXJ5Il19",
       "ok": true,
       "headers": [
        [
         "content-type",
         "application/json"
        ]
       ],
       "status": 200,
       "status_text": ""
      },
      "https://localhost:6006/data/plugin/text/text?markdown=true&run=Jun01_18-19-51_9b7435d1e69a&tag=args%2Ftext_summary": {
       "data": "[encoded data, 4892 characters]",
       "ok": true,
       "headers": [
        [
         "content-type",
         "application/json"
        ]
       ],
       "status": 200,
       "status_text": ""
      },
      "https://localhost:6006/data/plugin/hparams/experiment?request=%7B%22experimentName%22%3A%22%22%7D": {
       "data": "[encoded data, 19128 characters]",
       "ok": true,
       "headers": [
        [
         "content-type",
         "application/json"
        ]
       ],
       "status": 200,
       "status_text": ""
      },
      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   "source": [
    "training_args = transformers.TrainingArguments(\n",
    "    per_device_train_batch_size=1,\n",
    "    gradient_accumulation_steps=4,\n",
    "    num_train_epochs=1,\n",
    "    learning_rate=2e-4,\n",
    "    fp16=True,\n",
    "    save_total_limit=3,\n",
    "    logging_steps=1,\n",
    "    output_dir=OUTPUT_DIR,\n",
    "    max_steps=80,\n",
    "    optim=\"paged_adamw_8bit\",\n",
    "    lr_scheduler_type=\"cosine\",\n",
    "    warmup_ratio=0.05,\n",
    "    report_to=\"tensorboard\",\n",
    ")\n",
    "\n",
    "trainer = transformers.Trainer(\n",
    "    model=model,\n",
    "    train_dataset=data,\n",
    "    args=training_args,\n",
    "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
    ")\n",
    "model.config.use_cache = False\n",
    "trainer.train()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "G0tXhYO5ea5l",
    "outputId": "00c73bb3-0e4a-45d3-c235-9088b79477de"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='80' max='80' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [80/80 07:15, Epoch 4/5]\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>2.077400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.086700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.124100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.968100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2.048200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2.137500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.942300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.695200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.739100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.529400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.464600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.675800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.299600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.156000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.273800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.189400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.028300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.945700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.326200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.033200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.174500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.797100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.829100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.736800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.900600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.955600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.896100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.863500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.647400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.640400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.739600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.788800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.726700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.795300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.005300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.681900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.758200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.565200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.623800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.465700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.502900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.453800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.586900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.709400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.675100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.551800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.531900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.668400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.545100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.503300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.467800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.494700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.394500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.370800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.393300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.607500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.833000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.463800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.280700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.396900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.322900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.602100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.331300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.318100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.460900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.485200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.572600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.330900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.557900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.517200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.395900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.354400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.406600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.370200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.358900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.310800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.379100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.497100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "TrainOutput(global_step=80, training_loss=0.8479476287961006, metrics={'train_runtime': 441.1363, 'train_samples_per_second': 0.725, 'train_steps_per_second': 0.181, 'total_flos': 337002051969024.0, 'train_loss': 0.8479476287961006, 'epoch': 4.05})"
      ]
     },
     "metadata": {},
     "execution_count": 31
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Save Trained Model"
   ],
   "metadata": {
    "id": "XEMYxmFFZevu"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "model.save_pretrained(\"trained-model\")"
   ],
   "metadata": {
    "id": "__D-YiF4i4E-"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "model.push_to_hub(\n",
    "    \"curiousily/falcon-7b-qlora-chat-support-bot-faq\", use_auth_token=True\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 240,
     "referenced_widgets": [
      "1765001f2e8a45789c303bf25802ec26",
      "b9342727e5b04a31a44a2bb39eb9b972",
      "fb9cd1d7b199467ca84fdee089c3e616",
      "cc2c910395a14a1b876e76a9f1967159",
      "e6870d171e2e4f49b29940a584f8234f",
      "6a21b3db09d043a09364c677e380fd1a",
      "b47756ecdc5544fb881978069650dff7",
      "8623daa5b6104e01aa049cce796d57a5",
      "6a369f47577d4a1ab89fc34e893295e3",
      "ec6c022167c549949cc2d4261e6b388a",
      "defa462b8d0c425b8a3c5c10d3824449",
      "ed46a43aa3b74664b7be0c5393af55c7",
      "ea756bf0eec44265a894ca2c007ecdd3",
      "6e2ec6222dbc4dafbbe3379d89e74a33",
      "05c0d40b5c01447790d0ceccebc03d0f",
      "3e72185f6ba44316818d958ff112e6a5",
      "535b91b499d64a19a74ad64b96ff520b",
      "b20a32370ca94e4490ec2541fedf4629",
      "c770c0437f13471092d8b7bd92169f27",
      "19e71863a0384ec8851bde3abbb3fa68",
      "d0d5e191b9ea48fc83e8d14eef28807e",
      "55aa6981a6304872b253b5452dd0399f",
      "7cf8792fe92346e4be71b1cc0707c40f",
      "95ea93ef06164a6792df160fc5a856e1",
      "2c8a23857bfb41a28a4dcbaf360c5152",
      "d81c35bd6c964b8396a93ae5ce9b7035",
      "5f5378a779524d05bd130689944face8",
      "7a12995dd12f4aa8ac273ed26fe8c774",
      "e5a51c252b4642828528866ef7a71f61",
      "996b7bc4804747bc8e3d3bff8c09bc4e",
      "ef942a8f706549fd9d0ff97019b23d07",
      "1f5ec464af694baeb020c677c9804203",
      "05b4e2dcdb774de3847a1af4328a2d1e"
     ]
    },
    "id": "SBTcZs_EODfg",
    "outputId": "4727fb36-a7a7-4f27-f6c7-ce93ced846d9"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/_commit_api.py:232: UserWarning: About to update multiple times the same file in the same commit: 'adapter_config.json'. This can cause undesired inconsistencies in your repo.\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/_commit_api.py:232: UserWarning: About to update multiple times the same file in the same commit: 'adapter_model.bin'. This can cause undesired inconsistencies in your repo.\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": "1765001f2e8a45789c303bf25802ec26"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "adapter_model.bin:   0%|          | 0.00/18.9M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ed46a43aa3b74664b7be0c5393af55c7"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "adapter_model.bin:   0%|          | 0.00/18.9M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "7cf8792fe92346e4be71b1cc0707c40f"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/curiousily/falcon-7b-chat-support-bot-faq/commit/a928c86d139ef860caf33df5b9eeccdc61d3688a', commit_message='Upload model', commit_description='', oid='a928c86d139ef860caf33df5b9eeccdc61d3688a', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "metadata": {},
     "execution_count": 35
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Load Trained Model"
   ],
   "metadata": {
    "id": "KIlYhwJhZgjb"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "PEFT_MODEL = \"curiousily/falcon-7b-qlora-chat-support-bot-faq\"\n",
    "\n",
    "config = PeftConfig.from_pretrained(PEFT_MODEL)\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    config.base_model_name_or_path,\n",
    "    return_dict=True,\n",
    "    quantization_config=bnb_config,\n",
    "    device_map=\"auto\",\n",
    "    trust_remote_code=True,\n",
    ")\n",
    "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "\n",
    "model = PeftModel.from_pretrained(model, PEFT_MODEL)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
     "referenced_widgets": [
      "66408bee12974f96b42c7ebef03f5fb1",
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      "041ed7f21ecd4a7fbae240a5bd0c20a9",
      "3a083454009c431181869fd7bfd4ac9a",
      "fcb4d59661c44d27932e0194d6b0aa1c",
      "eee58e8c4ff641559649c3b82961a4c3",
      "b5887b1d286b4cdc9255f55c8334cfb3",
      "8cec295bad7447d1ab45aaa6c56bbef7",
      "fa64654a10814ad3a11aa11af4cf36ce",
      "f82c5b1d7d8f49e3ac3708b6a91f71f2",
      "9c1d89fdf66243548435317b75da3595",
      "ba6f3f3d687c474c9bacb2aff2f6cbfa",
      "da3010bd98e94beb9defdf825db3630d",
      "57a3f9fdddd0418aa96c99fd894726c3",
      "e9b09df570954dd78079b1c6e65429fa",
      "26482d9ce0944ce8adb7aff5d2174e06",
      "42a40156d8274b3195219f6811db3d92",
      "dd3861e35f264c629476755db522ea58",
      "ce1470c542724a97874b595d0002c2d7",
      "d4337a0b3e244c938a26bf4768a03854",
      "a2802ba1c11c4fe6b63f03a931ed7a67",
      "77e56c383fbf44d9ae314136db39adaf",
      "551736eb29504faaa884d6d00937e0d2",
      "1af09897d7e446449269106afcb079a8",
      "05cf083266ab4da6af75da41d784a973",
      "198f3f9771734a72b2a660ffeb178709",
      "2d68eaef329c417bad449df84e664463",
      "21f3873dc2914858b80617074a79e71d",
      "e79cca46e0a14d1c9d4e1ab17620862e",
      "d5e7ccf8574540a2b60138653d856e5d",
      "ab5655e1b7c948ddb07428d5d2e7d00f",
      "956740f8080e4838b8c5a67096c6894d"
     ]
    },
    "id": "owt5PmrcjG5C",
    "outputId": "9b79908f-5510-476b-d086-626686305190"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)/adapter_config.json:   0%|          | 0.00/410 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "66408bee12974f96b42c7ebef03f5fb1"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "9c1d89fdf66243548435317b75da3595"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading adapter_model.bin:   0%|          | 0.00/18.9M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "77e56c383fbf44d9ae314136db39adaf"
      }
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Inference"
   ],
   "metadata": {
    "id": "1XvuuaswZk4e"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "generation_config = model.generation_config\n",
    "generation_config.max_new_tokens = 200\n",
    "generation_config.temperature = 0.7\n",
    "generation_config.top_p = 0.7\n",
    "generation_config.num_return_sequences = 1\n",
    "generation_config.pad_token_id = tokenizer.eos_token_id\n",
    "generation_config.eos_token_id = tokenizer.eos_token_id"
   ],
   "metadata": {
    "id": "LAmXdHItPgQV"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "DEVICE = \"cuda:0\""
   ],
   "metadata": {
    "id": "qsS-RbE_UwJW"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "prompt = f\"\"\"\n",
    "<human>: How can I create an account?\n",
    "<assistant>:\n",
    "\"\"\".strip()\n",
    "\n",
    "encoding = tokenizer(prompt, return_tensors=\"pt\").to(DEVICE)\n",
    "with torch.inference_mode():\n",
    "    outputs = model.generate(\n",
    "        input_ids=encoding.input_ids,\n",
    "        attention_mask=encoding.attention_mask,\n",
    "        generation_config=generation_config,\n",
    "    )\n",
    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ivZI5MCcTCRC",
    "outputId": "1015d4d6-82ed-4650-d53e-b541bb8444e7"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<human>: How can I create an account?\n",
      "<assistant>: To create an account, please visit our sign-up page and enter your email address. Once you have completed the registration process, you will receive a confirmation email with instructions on how to activate your account. If you do not receive the email within a few minutes, please check your spam or junk folder. If you still cannot find it, contact our customer support team for assistance.\n",
      "CPU times: user 1min 1s, sys: 38.8 ms, total: 1min 1s\n",
      "Wall time: 1min 1s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def generate_response(question: str) -> str:\n",
    "    prompt = f\"\"\"\n",
    "<human>: {question}\n",
    "<assistant>:\n",
    "\"\"\".strip()\n",
    "    encoding = tokenizer(prompt, return_tensors=\"pt\").to(DEVICE)\n",
    "    with torch.inference_mode():\n",
    "        outputs = model.generate(\n",
    "            input_ids=encoding.input_ids,\n",
    "            attention_mask=encoding.attention_mask,\n",
    "            generation_config=generation_config,\n",
    "        )\n",
    "    response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "\n",
    "    assistant_start = \"<assistant>:\"\n",
    "    response_start = response.find(assistant_start)\n",
    "    return response[response_start + len(assistant_start) :].strip()"
   ],
   "metadata": {
    "id": "ZxDcYoX9Tor2"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "prompt = \"Can I return a product if it was a clearance or final sale item?\"\n",
    "print(generate_response(prompt))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5cao6k4nWdht",
    "outputId": "624d4145-4d16-467d-deff-fb1993673737"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Clearance and final sale items are typically non-returnable and non-refundable. Please review the product description or contact our customer support team for more information.\n",
      "If you have any questions about our return policy, please contact our customer support team for assistance. We will be happy to assist you with the process.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "prompt = \"What happens when I return a clearance item?\"\n",
    "print(generate_response(prompt))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C-INBeW-WmBS",
    "outputId": "1aa200f2-2d04-41b1-bb27-40ab74ccabff"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "If you return a clearance item, you will receive a refund for the discounted amount. Please note that clearance items are final sale and cannot be returned for a refund after the return deadline.\n",
      "If you have any questions about our return policy, please contact our customer support team for assistance.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "prompt = \"How do I know when I'll receive my order?\"\n",
    "\n",
    "print(generate_response(prompt))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O-UNWcvAVaRu",
    "outputId": "c0cd09ff-87dc-4dae-e5ee-d0f9f9fb230e"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Once your order is placed, you will receive a confirmation email with tracking information. Please allow up to 24 hours for the tracking information to become available. If you do not receive your tracking information within this time frame, please contact our customer support team. We will assist you with the tracking information and resolve the issue.\n"
     ]
    }
   ]
  }
 ]
}