tutorials/fine-tuning_LLM_with_PEFT_QLoRA/main.ipynb

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    "## Finetune Llama-2-7b using QLora on a Google colab\n",
    "\n",
    "Running large language models (LLMs) requires a lot of GPU power and memory, which can be costly. To improve performance and reduce costs, lightweight LLM models are being explored. This blog will cover key techniques for deploying LLMs more efficiently and affordably.\n",
    "\n",
    "This example shows **How to fine-tine Llama-2-7B model using Instruction tuning with PEFT and QLoRA**.\n",
    "\n",
    "\n",
    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)"
   ],
   "metadata": {
    "id": "C2EgqEPDQ8v6"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Setup\n",
    "\n",
    "Run the cells below to setup and install the required libraries. For our experiment we will need `accelerate`, `peft`, `transformers`, `datasets` and TRL to leverage the recent [`SFTTrainer`](https://huggingface.co/docs/trl/main/en/sft_trainer). We will use `bitsandbytes` to [quantize the base model into 4bit](https://huggingface.co/blog/4bit-transformers-bitsandbytes). We will also install `einops` as it is a requirement to load Falcon models."
   ],
   "metadata": {
    "id": "i-tTvEF1RT3y"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mNnkgBq7Q3EU",
    "outputId": "9cc72eec-a77f-402b-8fd5-57a14653383c"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m150.9/150.9 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.4/8.4 MB\u001b[0m \u001b[31m30.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m270.9/270.9 kB\u001b[0m \u001b[31m31.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m32.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.7/79.7 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m14.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.0/105.0 MB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m82.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m196.4/196.4 kB\u001b[0m \u001b[31m23.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m254.1/254.1 kB\u001b[0m \u001b[31m22.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h"
     ]
    }
   ],
   "source": [
    "# install required libraries\n",
    "\n",
    "!pip install -q -U trl transformers accelerate git+https://github.com/huggingface/peft.git\n",
    "!pip install -q datasets bitsandbytes einops wandb"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# import libraries and modules\n",
    "\n",
    "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",
    "\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",
    ")"
   ],
   "metadata": {
    "id": "ODB783kwKlgJ"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Connect with HF account"
   ],
   "metadata": {
    "id": "hzFhMow6ZY44"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Follow the Instructions to generate huggingfaces token with write access\n",
    "\n",
    "Create an account, if you don't have account in Hugging Face. If it is already present then\n",
    "\n",
    "***Go to Profile -> Access Tokens -> Create a token with write access -> Copy it and use it for login***"
   ],
   "metadata": {
    "id": "Av5JpWe1LTXE"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# login with hugging face token\n",
    "notebook_login()"
   ],
   "metadata": {
    "id": "euiJoW3-Kliu",
    "outputId": "2004d455-2f8d-45ce-973a-cf265d769f85",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145,
     "referenced_widgets": [
      "db0da7cffbea4d97a21968560229cc63",
      "127213df2edb4289b139e64ebfbb935f",
      "bd14014f8d854e5894faa105830cd657",
      "64c3c85242b2491986074f35d502159c",
      "2c56801548ca4329a24d83e9548653e4",
      "e69c61e625f14e0cb1e986dabab577f6",
      "5dbc8bd6ddd5408499e3a69fd1e421f5",
      "82956148127e4286abbae222449f0ce4",
      "5fb09536c5774f649a898950c3729088",
      "1495f2e0648549afa30100f8167f88be",
      "84b96a8b50394d1da8465a8722440fbe",
      "faf00a2cb92047eb87905d41855e4c34",
      "1b6fbc996c5a4e5fbce0e5cfcc764f57",
      "f50495d0cc3d42419796c3a1f9627644",
      "b51dc9418dac4dc9910c68c9ed0af11d",
      "22a7fd916801433c83857efd160b8504",
      "e9a1402d61204f22b691cc1b8f3fab98",
      "ac82aa13b6a64cd2ad3fd79abb52ff84",
      "bc86932701f64eb1b84b873d1b9ebed9",
      "70a04906833e4855a2c9ebcff55f76a9",
      "68be93836c054703b4da20fe1d24146b",
      "085a92d31c874f9a8bc120ee0f5553c8",
      "19c133334a2d4f8e863da22ba8e36727",
      "56021216344347f8ad0d91787bf42074",
      "f6690cc9083243568699024635372541",
      "db78a7dabf06431db8d380042a040bdc",
      "8d709eace6d94abcb4481c06eaba168e",
      "7dc5279d1c61472ca4cb2dd87a1ec73d",
      "27187ef4ee04435ca4fbbc8e17ba1ef7",
      "4fc760ceaaea4ec7ba282d634bc3ed95",
      "329bf711b3d046b3b4dfeff7eb89a751",
      "0fea75df70d9444c8dcc5407184f61fe"
     ]
    }
   },
   "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…"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "db0da7cffbea4d97a21968560229cc63"
      }
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# check for VRAM of GPU\n",
    "!nvidia-smi"
   ],
   "metadata": {
    "id": "tY_wTbS7PETU",
    "outputId": "347dfec4-7a8a-4e24-aa2a-d776c35753e6",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Wed Jan 24 11:09:45 2024       \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\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   46C    P8              10W /  70W |      3MiB / 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": "markdown",
   "source": [
    "### Download data for tuning"
   ],
   "metadata": {
    "id": "daHiXTX19JEc"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# download ecommerce-faq.json\n",
    "\n",
    "!gdown 1tiAscG941evQS8RzjznoPu8meu4unw5A"
   ],
   "metadata": {
    "id": "UVosPiQTKllv",
    "outputId": "a2708f06-c47f-4425-9882-8c22c3471f6f",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1tiAscG941evQS8RzjznoPu8meu4unw5A\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, 45.3MB/s]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Data loading and understanding"
   ],
   "metadata": {
    "id": "7YRSakbl9PQ8"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# loading and reading values in ecommerce-faq.json\n",
    "\n",
    "with open(\"ecommerce-faq.json\") as json_file:\n",
    "    data = json.load(json_file)"
   ],
   "metadata": {
    "id": "8WSc6kUyKloc"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "pprint(data[\"questions\"][0], sort_dicts=False)"
   ],
   "metadata": {
    "id": "_17jsWYqKlro",
    "outputId": "556130be-dba9-4531-a4e1-ed9db674c16b",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "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": {
    "id": "XBsoKRdsKluv",
    "outputId": "bedfd712-a80c-4786-bc19-bc2f48fb7e55",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "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\"][3], sort_dicts=False)"
   ],
   "metadata": {
    "id": "E29dFIihKlxW",
    "outputId": "de95a5e3-2513-44fb-af3b-a9b4278383fe",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "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": [
    "# reading dataset.json for Q&A\n",
    "with open(\"dataset.json\", \"w\") as f:\n",
    "    json.dump(data[\"questions\"], f)"
   ],
   "metadata": {
    "id": "9cE25RS5Mk1D"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "pd.DataFrame(data[\"questions\"]).head()"
   ],
   "metadata": {
    "id": "AgP372eXMk4N",
    "outputId": "9ef08fe0-347e-45a5-db78-479ae4994480",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                              question  \\\n",
       "0         How can I create an account?   \n",
       "1  What payment methods do you accept?   \n",
       "2            How can I track my order?   \n",
       "3          What is your return policy?   \n",
       "4               Can I cancel my order?   \n",
       "\n",
       "                                              answer  \n",
       "0  To create an account, click on the 'Sign Up' b...  \n",
       "1  We accept major credit cards, debit cards, and...  \n",
       "2  You can track your order by logging into your ...  \n",
       "3  Our return policy allows you to return product...  \n",
       "4  You can cancel your order if it has not been s...  "
      ],
      "text/html": [
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       "    <div>\n",
       "<style scoped>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-14a3910e-b017-4aeb-a630-2da7370e37f1 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-14a3910e-b017-4aeb-a630-2da7370e37f1');\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",
       "\n",
       "\n",
       "<div id=\"df-8797e326-54cf-49d9-91ff-b19046e3e21c\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8797e326-54cf-49d9-91ff-b19046e3e21c')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-8797e326-54cf-49d9-91ff-b19046e3e21c button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "We’re using a sharded model, which means the model is split into multiple parts—around 14 pieces in this case. This helps accelerate the process by allowing different pieces to be loaded into various memory types, like GPU or CPU memory. This way, you can work with and fine-tune a large model even with limited memory, which is why we use the sharded approach."
   ],
   "metadata": {
    "id": "ZXUqtrpcMpDN"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Changing the quantization type\n",
    "\n",
    "The 4bit integration comes with 2 different quantization types: FP4 and NF4. The NF4 dtype stands for Normal Float 4 and is introduced in the [QLoRA paper](https://arxiv.org/abs/2305.14314)\n",
    "\n",
    "YOu can switch between these two dtype using `bnb_4bit_quant_type` from `BitsAndBytesConfig`. By default, the FP4 quantization is used."
   ],
   "metadata": {
    "id": "wEXXPFrOaSZz"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# Load Llama-7b bf16 sharded Model & tokenizer\n",
    "\n",
    "MODEL_NAME = \"TinyPixel/Llama-2-7B-bf16-sharded\"\n",
    "\n",
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=torch.bfloat16,\n",
    ")\n",
    "\n",
    "# model\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    MODEL_NAME,\n",
    "    trust_remote_code=True,\n",
    "    quantization_config=bnb_config,\n",
    ")\n",
    "\n",
    "# tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
    "tokenizer.pad_token = tokenizer.eos_token"
   ],
   "metadata": {
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    },
    "outputId": "1242b674-f165-4e2a-aa62-6e1b51918c40"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
      "You will be able to reuse this secret in all of your notebooks.\n",
      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
      "  warnings.warn(\n"
     ]
    },
    {
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  },
  {
   "cell_type": "code",
   "source": [
    "# helper function to print number of trainable parameters\n",
    "\n",
    "\n",
    "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": "ZHizPE78Mk-r"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "model.gradient_checkpointing_enable()\n",
    "model = prepare_model_for_kbit_training(model)"
   ],
   "metadata": {
    "id": "HptrfjwCMlBs"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Lora Config\n",
    "\n",
    "LoraConfig allows you to control how LoRA is applied to the base model through the following parameters - \\\n",
    "\n",
    "***r*** - the rank of the update matrices, expressed in int. Lower rank results in smaller update matrices with fewer trainable parameters. \\\n",
    "\n",
    "***target_modules*** - The modules (for example, attention blocks) to apply the LoRA update matrices. \\\n",
    "\n",
    "***alpha***  - LoRA scaling factor. \\\n",
    "\n",
    "***bias*** - Specifies if the bias parameters should be trained. Can be 'none', 'all' or 'lora_only'. \\\n",
    "\n",
    "***modules_to_save*** - List of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. These typically include model’s custom head that is randomly initialized for the fine-tuning task.\n",
    "***layers_to_transform*** - List of layers to be transformed by LoRA. If not specified, all layers in target_modules are transformed. \\\n",
    "\n",
    "***layers_pattern*** - Pattern to match layer names in target_modules, if layers_to_transform is specified. By default PeftModel will look at common layer pattern (layers, h, blocks, etc.), use it for exotic and custom models. \\\n",
    "\n",
    "***rank_pattern*** - The mapping from layer names or regexp expression to ranks which are different from the default rank specified by r. \\\n",
    "\n",
    "***alpha_pattern*** - The mapping from layer names or regexp expression to alphas which are different from the default alpha specified by lora_alpha."
   ],
   "metadata": {
    "id": "6fJ9W-hje4sz"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "from peft import LoraConfig, get_peft_model\n",
    "\n",
    "# set Loraconfig parameter values\n",
    "\n",
    "lora_alpha = 16\n",
    "lora_dropout = 0.1\n",
    "lora_r = 64\n",
    "\n",
    "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",
    "model = get_peft_model(model, config)\n",
    "print_trainable_parameters(model)"
   ],
   "metadata": {
    "id": "lSXo09-4MlEi",
    "outputId": "d01356bc-b71f-46b6-cb8a-7dc8d2685555",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Trainable params: 33554432 || All params: 3533967360 || Trainable%: 0.9494833591219133\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Inference Before Training"
   ],
   "metadata": {
    "id": "9UnhUWHm-YHh"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "prompt = f\"\"\"\n",
    ": How can I create an account?\n",
    ":\n",
    "\"\"\".strip()\n",
    "print(prompt)"
   ],
   "metadata": {
    "id": "xxyk3cGDMlJO",
    "outputId": "ba282dee-f7a6-4574-ebe8-efaaf758f52d",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      ": How can I create an account?\n",
      ":\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# change configuration for generation\n",
    "\n",
    "generation_config = model.generation_config\n",
    "generation_config.max_new_tokens = 80\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": "Zal7MzltMlNc"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# generation configurations\n",
    "generation_config"
   ],
   "metadata": {
    "id": "AF9pGCJ2MlUH",
    "outputId": "46da0b1a-00b2-42c0-b78c-3ba4f40345db",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "GenerationConfig {\n",
       "  \"bos_token_id\": 1,\n",
       "  \"eos_token_id\": 2,\n",
       "  \"max_new_tokens\": 80,\n",
       "  \"pad_token_id\": 2,\n",
       "  \"temperature\": 0.7,\n",
       "  \"top_p\": 0.7\n",
       "}"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "# Specify the target device for model execution, typically a GPU.\n",
    "device = \"cuda:0\"\n",
    "\n",
    "# Tokenize the input prompt and move it to the specified device.\n",
    "encoding = tokenizer(prompt, return_tensors=\"pt\").to(device)\n",
    "\n",
    "# Run model inference in evaluation mode (inference_mode) for efficiency.\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",
    "\n",
    "\n",
    "# Decode the generated output and print it, excluding special tokens.\n",
    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
   ],
   "metadata": {
    "id": "cComMNlMNIYx",
    "outputId": "69fc1b92-880f-43e8-fced-0d138c0ba8cf",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:392: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.7` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:397: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.7` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      ": How can I create an account?\n",
      ": How can I create an account?\n",
      ": How can I create an account? : How can I create an account?\n",
      "CPU times: user 6.25 s, sys: 826 ms, total: 7.08 s\n",
      "Wall time: 11.4 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Build HuggingFace Dataset format"
   ],
   "metadata": {
    "id": "yostlhu9PKF2"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "data = load_dataset(\"json\", data_files=\"dataset.json\")"
   ],
   "metadata": {
    "id": "d_WmYk94NIfa",
    "outputId": "7490170d-8b15-4e8f-a366-9ce1fd4755b4",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "0c2449f48a474e6f93cde3d312d3a5dc",
      "5ba64ba2f4c24866b7adbb99fd40d314",
      "4213e9108c5e4974b4b62401c4687447",
      "cc4d09c3a9214bfb867eec0af3f2d7b1",
      "a6c7363109fa41b5ad9dc462165c013a",
      "ec0c6f59b8bf4eb6bf0bfec533eab08d",
      "dc589955e6744383ad572bd704ab3328",
      "feea409ab6e444f992ea4fa895e66169",
      "e9429b3be5db42db9fda06f0f5aa34e9",
      "d07fb77c8e10407dbeda4130156b8f2a",
      "16a438b7ce6342b3b0a1b4f3f87d248b"
     ]
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "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": "0c2449f48a474e6f93cde3d312d3a5dc"
      }
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "data"
   ],
   "metadata": {
    "id": "TJkbZpXzNIiS",
    "outputId": "eef50255-f92f-49ae-b9ba-bfed60617139",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['answer', 'question'],\n",
       "        num_rows: 79\n",
       "    })\n",
       "})"
      ]
     },
     "metadata": {},
     "execution_count": 21
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "data[\"train\"][0]"
   ],
   "metadata": {
    "id": "fSHb0QqWNIlh",
    "outputId": "47a0dea2-5958-48b2-f7bc-b0ac63123bc7",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'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.\",\n",
       " 'question': 'How can I create an account?'}"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def generate_prompt(data_point):\n",
    "    return f\"\"\"\n",
    ": {data_point[\"question\"]}\n",
    ": {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": "6PGm88MhNIp6"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "data = data[\"train\"].shuffle().map(generate_and_tokenize_prompt)"
   ],
   "metadata": {
    "id": "byzQ1CmQNItJ",
    "outputId": "5f275dc5-ba06-475d-bdb9-836f25168358",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67,
     "referenced_widgets": [
      "7689dc301af34e50b31eb03199044994",
      "7997a206e4e9417ca48175d7c958aa49",
      "10cde979ec914e6eb52f297db23c297d",
      "779616cd217a4d1382f3955611276d84",
      "b621686032114e0dbd15d9e9cadd8c1b",
      "b11780aa116a493d92570db8a16013e3",
      "cbe0d6ad82e14f3a9cdac3ef93879ac4",
      "fcd98d26ebf84cc4b3674e0b341de679",
      "a4082600c40f486eb1b82df41dfd5d8d",
      "3754c400c4c742c19e0fcea32d92515f",
      "08b6cdda31414d74b07d890b62d4c8ba"
     ]
    }
   },
   "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": "7689dc301af34e50b31eb03199044994"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "data"
   ],
   "metadata": {
    "id": "xh8z2xhHKl0z",
    "outputId": "6514f74f-06b9-442d-da26-f3841cd46ddb",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['answer', 'question', 'input_ids', 'attention_mask'],\n",
       "    num_rows: 79\n",
       "})"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "OUTPUT_DIR = \"experiments\""
   ],
   "metadata": {
    "id": "oNSwV8ECQ3pe"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# training\n",
    "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": {
    "id": "TO-zrLspQ347",
    "outputId": "434b300d-f633-4307-d9d2-53042d3ad88f",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='80' max='80' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [80/80 07:21, 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>2.275600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.245900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.933500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.858800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2.012600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.801800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.794000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.489300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.587700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.560400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.471600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.551800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.598900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.403500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.195500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.334300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.191300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.072000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.151500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.109000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.135800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.122000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.953200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.027600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.940800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.907100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.784400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.880200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.014100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.843800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.039000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.733400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.676000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.628600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.906400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.530600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.678700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.595400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.748500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.590200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.563200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.639400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.513500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.645800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.542300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.364400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.481800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.647700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.489400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.634600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.365600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.420700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.487100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.533600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.361700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.460900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.515300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.547600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.514300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.547600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.409700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.347000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.467800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.429700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.441100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.406900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.505200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.405800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.427400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.528000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.290200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.301500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.484300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.383900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.444400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.424000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.486000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.480600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.397400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.419400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "TrainOutput(global_step=80, training_loss=0.8391439635306597, metrics={'train_runtime': 447.6633, 'train_samples_per_second': 0.715, 'train_steps_per_second': 0.179, 'total_flos': 649997819142144.0, 'train_loss': 0.8391439635306597, 'epoch': 4.05})"
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "I trained it for 100 epochs, and as you can observe, the loss consistently decreases, indicating room for further improvement.\n",
    "\n",
    "NOTE: ***Consider extending the training to a higher number of epochs for potential enhancements***"
   ],
   "metadata": {
    "id": "NKLu3hYCYWbW"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Save model in local system"
   ],
   "metadata": {
    "id": "Bm-4ny2SYgYz"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "model.save_pretrained(\"trained-model\")"
   ],
   "metadata": {
    "id": "YQ4VipiaQ38Q"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Push trained model in Hugging face\n",
    "\n",
    "NOTE: ***Here you have to change directory where you want to push your model***.\n",
    "\n",
    "For me it is \"Prasant/Llama2-7b-qlora-chat-support-bot-faq\""
   ],
   "metadata": {
    "id": "gEWFr9vKYbvI"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "model.push_to_hub(\"Prasant/Llama2-7b-qlora-chat-support-bot-faq\")"
   ],
   "metadata": {
    "id": "bvkTqEZFQ3_W",
    "outputId": "4d5e4b9e-a8ec-4514-e8e6-a390dfa39dd9",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85,
     "referenced_widgets": [
      "c91ff953b85847fd9bcbc952b259d13e",
      "80055b5100d0410ab59ba6c000e5f65c",
      "eb05122c60014feca974b9be59a89570",
      "d3a6a5bcce6a4e7eaa2db04a53f5b933",
      "c5ccd8c9f94749d391fb0815180cc7ed",
      "64d26285c5d5413dab6ff9649e5b3b11",
      "100084059b6d41858478218c3ffec02a",
      "157518129a4d46c2b23b86223ae34ee4",
      "e44f488773994690a9505db3c7f5ad6f",
      "d6e500ae93e04beb8d5b80162df8cf9a",
      "4f4ac78e20d64c12a4d3d394f7ab78ae"
     ]
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "adapter_model.safetensors:   0%|          | 0.00/134M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c91ff953b85847fd9bcbc952b259d13e"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/Prasant/Llama2-7b-qlora-chat-support-bot-faq/commit/afdc083726f49ccf925eda01e564e2a9520d92f3', commit_message='Upload model', commit_description='', oid='afdc083726f49ccf925eda01e564e2a9520d92f3', pr_url=None, pr_revision=None, pr_num=None)"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      }
     },
     "metadata": {},
     "execution_count": 29
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "In our approach, we've split the large model TinyPixel/Llama-2-7B-bf16 into more than 14 smaller parts, a method known as sharding. This strategy works well with the `accelerate` framework by huggingface.\n",
    "\n",
    "Each shard holds part of the model's data, and Accelerate helps distribute these parts across different memory types, like GPU and CPU. This way, we can handle large models without needing too much memory."
   ],
   "metadata": {
    "id": "RFiESDCCRXG6"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Load pushed model\n",
    "\n",
    "Load model from the directory you pushed, for me it is \"Prasant/Llama2-7b-qlora-chat-support-bot-faq\""
   ],
   "metadata": {
    "id": "JuTrGGWg_zRY"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "PEFT_MODEL = \"Prasant/Llama2-7b-qlora-chat-support-bot-faq\"\n",
    "\n",
    "# loading trained model from hugging face\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": {
    "id": "Fq9phlfIQ4E5",
    "outputId": "69adcb7b-ee40-4834-adf4-2eca74ee5284",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
     "referenced_widgets": [
      "25e4e057e547452fa7870203ecf304af",
      "9d48543aaf23498f85b8ad162ed21a51",
      "1227f4ab5ce745898d952c79afe2e118",
      "c51db07388824a71980ea8b0734639d8",
      "70c4820f69984344acfe5becc487057a",
      "78477d0ebafe4bbe8f3404d846f05a0d",
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      "8dff8fc4d7f44193bc48e6121c1a63c2",
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      "0a2ba91f95e14c049d7616ed5cb0c73b",
      "804eec09657b479183c258825f10d02d",
      "f02fca9c66324f7ea611b29ddc862f75",
      "b4e8aece348f400089c3f37781c119d6",
      "fafb33d86b5a4da4ab37b121350cad8d",
      "1e0286d9297c4c859fbbce69bf123971",
      "1801009a0b30418d8fa4bc623cf290da",
      "174ea14f5eba4cfe8d68a26f7d239a08",
      "2134a7e4dca14686a564bd724efac364",
      "325bf139f1aa4dbc9bafe500c1f77172",
      "a16aa5dfef6049d5a3f71b7fc501ebb7",
      "d213614cb3d34bbc833327333a4e7964",
      "ee9f15735568419389482445f330a2cc",
      "c5f05746bd6e49df8035419a6e2d247b",
      "a81437a7cd1f44e693f9c43ded243d6b",
      "6c91ae9a068b4aed94df30c352825341",
      "0a34f2f771a947849b5e526a42584eb6",
      "1c1ab77f2f2b4560863ee8dd51add0cf",
      "335af2e8e0134e34b49328da24c73294",
      "61f6ab50052c475797c37936f5616ed0",
      "3e6d92c3b05d48fe8fc6f4a6da34e1ed",
      "5fae4a9789234b69bab42d72d5dd2136",
      "77884c522402433f95d1cda7d10c3f04"
     ]
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "adapter_config.json:   0%|          | 0.00/608 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "25e4e057e547452fa7870203ecf304af"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/14 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "804eec09657b479183c258825f10d02d"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "adapter_model.safetensors:   0%|          | 0.00/134M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ee9f15735568419389482445f330a2cc"
      }
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Do experiments with parameters and see what works for you and your data best"
   ],
   "metadata": {
    "id": "LP_1aPCYYyvx"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# model configuration, you can try changing these parameters\n",
    "generation_config = model.generation_config\n",
    "generation_config.max_new_tokens = 50\n",
    "\n",
    "# try using temperature parameter by uncommenting following\n",
    "# generation_config.temperature = 0.3\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": "O-umn8k_RZ7h"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# device configuration\n",
    "DEVICE = \"cuda:0\""
   ],
   "metadata": {
    "id": "dqP2fFuDRZ-o"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "prompt = f\"\"\"\n",
    ": How can I create an account?\n",
    ":\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": {
    "id": "TScya6W0RaDL",
    "outputId": "f4e599b4-9674-4f92-a2e7-7aabc66f28a3",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      ": How can I create an account?\n",
      ": To create an account, click on the 'Sign Up' button on the top right corner of the website. Follow the instructions to complete the registration process.\n",
      ": You can place an order by adding items to your shopping cart and proceeding to\n",
      "CPU times: user 4.37 s, sys: 252 ms, total: 4.62 s\n",
      "Wall time: 4.68 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# helper function to generate responses\n",
    "def generate_response(question: str) -> str:\n",
    "    prompt = f\"\"\"\n",
    ": {question}\n",
    ":\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 = \":\"\n",
    "    response_start = response.find(assistant_start)\n",
    "    return response[response_start + len(assistant_start) :].strip()"
   ],
   "metadata": {
    "id": "6ZQc3XvhRaGa"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# prompt\n",
    "prompt = \"Question: Can I return a product if it was a clearance or final sale item?\"\n",
    "print(generate_response(prompt))"
   ],
   "metadata": {
    "id": "IXiY44KGRaJv",
    "outputId": "357e5eaa-f72c-4025-d8a7-6e739e70ad8a",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Question: Can I return a product if it was a clearance or final sale item?\n",
      ": Clearance or final sale items are typically non-returnable. Please refer to the product description or contact our customer support team for specific return instructions.\n",
      ": You can request a return by contacting our customer support team. We will provide you with\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# prompt\n",
    "prompt = \"Question: What happens when I return a clearance item?\"\n",
    "print(generate_response(prompt))"
   ],
   "metadata": {
    "id": "yJE1BrB8RaM8",
    "outputId": "83ae00c0-bdff-40f0-ccfb-d37d6348cf61",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Question: What happens when I return a clearance item?\n",
      ": Clearance items are non-refundable and non-exchangeable. However, you can request a store credit for the full value of the item. Please contact our customer support team for assistance.\n",
      ": We accept returns within 30 days\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# prompt\n",
    "prompt = \"Question: How do I know when I'll receive my order?\"\n",
    "print(generate_response(prompt))"
   ],
   "metadata": {
    "id": "sDkq0PbhRw-2",
    "outputId": "be1753a4-2e91-42fb-dac6-cae6830594d4",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Question: How do I know when I'll receive my order?\n",
      ": Once you place an order, we will send you a confirmation email with your order details and estimated delivery time. You can track your order's progress by logging into your account or checking your order confirmation email.\n",
      ": If you need to\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "################ falcon with lama2\n",
    "# https://github.com/curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain/blob/master/07.falcon-qlora-fine-tuning.ipynb"
   ],
   "metadata": {
    "id": "3L3UxKlVRaPy"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## That's it; you can try to play with these hyperparameters to achieve better results 🎉\n",
    "\n",
    "If you liked this guide, do consider giving a 🌟 to LanceDB's [vector-recipes](https://github.com/lancedb/vectordb-recipes)"
   ],
   "metadata": {
    "id": "fspW3KiWaaCO"
   }
  }
 ]
}