{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WE5GJ6s7y0Xo"
      },
      "source": [
        "## Fine-tune large models using 🤗 [`peft`](https://github.com/huggingface/peft) adapters, [`transformers`](https://github.com/huggingface/transformers) & [`bitsandbytes`](https://github.com/TimDettmers/bitsandbytes)\n",
        "\n",
        "In this tutorial we will cover how we can fine-tune large language models using the very recent `peft` library and `bitsandbytes` for loading large models in **8-bit**.\n",
        "The fine-tuning method will rely on a recent method called \"Low Rank Adapters\" ([LoRA](https://arxiv.org/pdf/2106.09685.pdf)), instead of fine-tuning the entire model you just have to fine-tune these adapters and load them properly inside the model. \n",
        "After fine-tuning the model you can also share your adapters on the 🤗 Hub and load them very easily. Let's get started!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TfBzP8gWzkpv"
      },
      "source": [
        "### Install requirements\n",
        "\n",
        "First, run the cells below to install the requirements:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "otj46qRbtpnd",
        "outputId": "cbc6a9e9-2263-4c9d-f36a-7d28a1486513"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.2/92.2 MB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m474.6/474.6 kB\u001b[0m \u001b[31m27.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m219.1/219.1 kB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m287.1 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m16.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.5/212.5 kB\u001b[0m \u001b[31m31.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m20.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m70.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 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[32m114.5/114.5 kB\u001b[0m \u001b[31m17.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.6/149.6 kB\u001b[0m \u001b[31m21.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  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",
            "  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[32m7.8/7.8 MB\u001b[0m \u001b[31m106.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m82.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q bitsandbytes datasets accelerate loralib einops\n",
        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Q9oFzZdcD6T",
        "outputId": "046873a7-32aa-458b-94c3-2ab760033b56"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "4e0f42040d8e4c76bb20363a9e4046cf",
            "ae94034b488843abbe7ac4c98e3e5d4c",
            "12b608ed159145aca6da27c36ec4a9b1",
            "76ed1fdaf6104c5986de9c6d5f42880e",
            "808edf7de7f947409c9582e11d135644",
            "34703094b08548aab6e5fe18f150eae7",
            "24bf2aa87ac54d5f97227f17a23540af",
            "e09b9f4dabd14b819cd521c1c2ccd6eb",
            "7e10a8c02e894209af442c9be36deb1a",
            "e63c2a186a484d57976539b8e4921b54",
            "b4e9ac74df32415aa71d3d2d14a892c3",
            "39e1e81a2b9948aa91fd85ff9d7c0805",
            "ac226b9296364042acdd608dfd39948d",
            "a1bab69649494c4991ab120b3e2b53c4",
            "b396e65fd46e40abb24e4475bbcad684",
            "ccc2e887ddea4d9ebb84d530c0ab286b",
            "d93cd35a1bae417588ed29a9502bdd91",
            "c56f29f847fa4cb1a8d5ea366751690d",
            "d2f7ddbf5bb14d388d051298626232c8",
            "e096ca2e6bc54addab358dc4d7586e03",
            "e9cbde63bf2647ab8ef00d317bd6fdc1",
            "ac6bae317cc1409c8c5a9b5771065c71",
            "5b5a0ec5cb7846c39e86805c209df940",
            "ac5748211a4148e18964fc8fd9cb3d3b",
            "699c180ffc134b69879ef4fcc5433096",
            "facfba04f0e644978be531565f471d63",
            "5e7ad5db85ba4d0fa40f0f6506b943d4",
            "73ee75e82f5e491f86cefee05928be61",
            "8e2330ebd6b744b9aa884f625e135406",
            "0274218ecb194d9fab451781c32d78e6",
            "c651075f3e0c49558ea9d11d0ada548b",
            "3f8ee32d95144035a3f3c4d4ac6527b1"
          ]
        },
        "id": "rTdXkcWecz3s",
        "outputId": "c28aec19-0116-4cde-8b84-1231747850dc"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4e0f42040d8e4c76bb20363a9e4046cf",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I2-xmZCnVDg_",
        "outputId": "ea447680-2ff9-49de-c9a7-7c089a6d5cc0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mon May 29 17:58:32 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  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   33C    P0    45W / 400W |      0MiB / 40960MiB |      0%      Default |\n",
            "|                               |                      |             Disabled |\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"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "koRYhzEOSyb0",
        "outputId": "198ad06e-9859-4a15-e61e-bdbd34fb4dfb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please run\n",
            "\n",
            "python -m bitsandbytes\n",
            "\n",
            " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 8.0\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('http'), PosixPath('8013'), PosixPath('//172.28.0.1')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-a100-s-2r76248p94k6f --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so'), PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
            "Either way, this might cause trouble in the future:\n",
            "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
            "  warn(msg)\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import bitsandbytes as bnb\n",
        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8vHtolPVZJj",
        "outputId": "9c87cc43-707d-4352-d7fb-9019583b6faf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{0: '37GB'}"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "free_in_GB = int(torch.cuda.mem_get_info()[0] / 1024**3)\n",
        "max_memory = f\"{free_in_GB-2}GB\"\n",
        "\n",
        "n_gpus = torch.cuda.device_count()\n",
        "max_memory = {i: max_memory for i in range(n_gpus)}\n",
        "max_memory"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FOtwYRI3zzXI"
      },
      "source": [
        "### Model loading"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528,
          "referenced_widgets": [
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            "f89fe537ce52412d9c5c6bd753cf759f",
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            "cab3e3a4832f4293b50a0c3f46604984",
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            "4c8022ba170347f9823ff6fdf1d613f5",
            "2a737213d27a44eeba04b67ebf94ea4f",
            "c6c80f07c6554654bd1740c58b91a310",
            "9adeac828ac340a6b7d157d942ba5220",
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            "436eabe55ee84b5e955c5be6f39e8aac",
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            "8579f3eaa39b4c00ae9fc87dcee35850",
            "4b466ae97ee346d281645b733a9a2001",
            "3d87dbea35274c21ba114be911776893",
            "d4ecb2ac67b543e884c2c9a070b6d730",
            "681efa1128404f7db16b75258a787617",
            "85a046a3b59b40fbb42c8ee8824e9eba",
            "362f568b791241a88db381d354013c63",
            "cc4572ad6d074e1a829a109e7b2fd5c1",
            "7d5a2b9f3d2b405c81de8d09f777288a",
            "2182ab4bf49b405b8a61f9407bec9785",
            "e568ec3d29f94595afcc878bcc71a405",
            "870d6556d9394d4086a7fcba614f1018",
            "cd1a2cfad705474bb12765dab71c2829",
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        "id": "cg3fiQOvmI3Q",
        "outputId": "1c6e5efb-ffec-4d0f-ebaf-6893cecb6072"
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      "outputs": [
        {
          "data": {
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              "version_major": 2,
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            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
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            "text/plain": [
              "Downloading (…)/configuration_RW.py:   0%|          | 0.00/2.61k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "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"
          ]
        },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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          "output_type": "stream",
          "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"
          ]
        },
        {
          "data": {
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              "model_id": "e568ec3d29f94595afcc878bcc71a405",
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          },
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              "model_id": "8bab092b4a4043ba88a9a9000a93d7cd",
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          },
          "metadata": {},
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        },
        {
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              "model_id": "6562ce5549064c8986704bc876867b96",
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          },
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        },
        {
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            ]
          },
          "metadata": {},
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        },
        {
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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      ],
      "source": [
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    \"tiiuae/falcon-7b\",\n",
        "    load_in_8bit=True,\n",
        "    device_map={\"\":0},\n",
        "    trust_remote_code=True,\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\n",
        "    \"tiiuae/falcon-7b\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fTSZntA1iUG"
      },
      "source": [
        "### Prepare model for training\n",
        "\n",
        "Some pre-processing needs to be done before training such an int8 model using `peft`, therefore let's import an utiliy function `prepare_model_for_kbit_training` that will: \n",
        "- Casts all the non `int8` modules to full precision (`fp32`) for stability\n",
        "- Add a `forward_hook` to the input embedding layer to enable gradient computation of the input hidden states\n",
        "- Enable gradient checkpointing for more memory-efficient training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "T-gy-LxM0yAi"
      },
      "outputs": [],
      "source": [
        "from peft import prepare_model_for_kbit_training\n",
        "\n",
        "model.gradient_checkpointing_enable()\n",
        "model = prepare_model_for_kbit_training(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KwOTr7B3NlM3"
      },
      "source": [
        "### Apply LoRA\n",
        "\n",
        "Here comes the magic with `peft`! Let's load a `PeftModel` and specify that we are going to use low-rank adapters (LoRA) using `get_peft_model` utility function from `peft`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "4W1j6lxaNnxC"
      },
      "outputs": [],
      "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",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4iwHGzKBN6wk",
        "outputId": "2404f68f-1a52-4f02-c1ed-3d5688eabfa2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "trainable params: 4718592 || all params: 6926439296 || trainable%: 0.06812435363037071\n"
          ]
        }
      ],
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdnQamMZTEXq",
        "outputId": "e6fd8130-4f20-4218-ceab-9d8466b7457e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch.float32 300487552 0.04338268757708318\n",
            "torch.int8 6625951744 0.9566173124229168\n"
          ]
        }
      ],
      "source": [
        "# Verifying the datatypes.\n",
        "dtypes = {}\n",
        "for _, p in model.named_parameters():\n",
        "    dtype = p.dtype\n",
        "    if dtype not in dtypes:\n",
        "        dtypes[dtype] = 0\n",
        "    dtypes[dtype] += p.numel()\n",
        "total = 0\n",
        "for k, v in dtypes.items():\n",
        "    total += v\n",
        "for k, v in dtypes.items():\n",
        "    print(k, v, v / total)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QdjWif4CVXR6"
      },
      "source": [
        "### Load dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "AwYPCVsRTO-Y"
      },
      "outputs": [],
      "source": [
        "import transformers\n",
        "from datasets import load_dataset, Dataset\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "-wNSUnrBGquS"
      },
      "outputs": [],
      "source": [
        "# extract, load, and transform OpenAssistant/oasst1 chatbot dataset\n",
        "\n",
        "# set some pandas options to make the output more readable\n",
        "pd.set_option(\"display.max_rows\", 500)\n",
        "pd.set_option(\"display.max_columns\", 500)\n",
        "pd.set_option(\"display.width\", 1000)"
      ]
    },
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          ]
        },
        "id": "2BiQEL-uHK69",
        "outputId": "79a9b379-167d-4a3f-e605-4c1988ae248d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a4bc0e09e5384ee79da6ccb0a8d85bda",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/10.2k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset None/None to /root/.cache/huggingface/datasets/OpenAssistant___parquet/OpenAssistant--oasst1-2960c57d7e52ab15/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a2223cc92bb948f586f211a03895acb0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b26b354666274481b3f05be8a008d115",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/39.5M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "863eddd2b314429eba6627592ab3c900",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/2.08M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6c47e8436380495baf123049e0910efb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "75fecf335af94d43b1f425e8b7f308dd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split:   0%|          | 0/84437 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c8bb2b3b179f43f5a22b3ac2ab56425e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating validation split:   0%|          | 0/4401 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/OpenAssistant___parquet/OpenAssistant--oasst1-2960c57d7e52ab15/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2009b596834548849d9a9d9200bbb30e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# load dataset from huggingface datasets\n",
        "ds = load_dataset(\"OpenAssistant/oasst1\")\n",
        "# lets convert the train dataset to a pandas df\n",
        "df = ds[\"train\"].to_pandas()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "0DhbpL1Ffkko"
      },
      "outputs": [],
      "source": [
        "# lets grab the message trees to train on \n",
        "message_tree_ids = np.unique(np.array(df[\"message_tree_id\"]))\n",
        "messages = {}\n",
        "messages['message_tree_id'] = []\n",
        "messages['message_tree_text'] = []\n",
        "\n",
        "for message_tree_id in message_tree_ids:\n",
        "    try:\n",
        "        # look at all data for this message tree\n",
        "        one_message_tree = df.query(f\"message_tree_id == '{message_tree_id}'\").sort_values(\"created_date\")\n",
        "        text = \"\"\n",
        "    \n",
        "        # root message\n",
        "        text += \"<human>: \" + one_message_tree.iloc[0].text\n",
        "        # find root message's children\n",
        "        children = one_message_tree[one_message_tree.parent_id == one_message_tree.iloc[0].message_id]\n",
        "        # find root message's top ranked child:\n",
        "        child = children[children['rank'] == 0.0]\n",
        "        text += '\\n' + \"<bot>: \" + child.iloc[0].text\n",
        "    \n",
        "        # proceed through rest of the above message tree until completion\n",
        "        flag=True\n",
        "        while flag:\n",
        "            try:\n",
        "                # find next prompt\n",
        "                children = one_message_tree[one_message_tree.parent_id == child.message_id.iloc[0]]\n",
        "                children.index\n",
        "                one_message_tree.loc[children.index].iloc[0].role\n",
        "                text += '\\n' + \"<human>: \" + one_message_tree.loc[children.index].iloc[0].text\n",
        "    \n",
        "                # find next children\n",
        "                children = one_message_tree[one_message_tree.parent_id == one_message_tree.loc[children.index].iloc[0].message_id]\n",
        "                children\n",
        "                # find top ranked child:\n",
        "                child = children[children['rank'] == 0.0]\n",
        "                text += '\\n' + \"<bot>: \" + child.iloc[0].text\n",
        "            except:\n",
        "                flag=False\n",
        "    \n",
        "        messages['message_tree_id'].append(message_tree_id)\n",
        "        messages['message_tree_text'].append(text)\n",
        "\n",
        "    except IndexError:\n",
        "        pass\n",
        "\n",
        "message_df = pd.DataFrame.from_dict(messages)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dCBMejldfknP",
        "outputId": "62ab0bc2-bf45-472a-ed9b-48c23ef90d07"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<human>: What is the capital of Japan?\n",
            "<bot>: Tokyo is the capital of Japan.\n",
            "<human>: And what is the capital of the Moon?\n",
            "<bot>: Since the Moon is not currently inhabited by humans, there is currently no capital of the Moon.\n",
            "\n",
            "\n",
            "message_tree_id                   01505c0f-2d68-4206-acfc-7ab39033c75a\n",
            "message_tree_text    <human>: What is the capital of Japan?\\n<bot>:...\n",
            "Name: 41, dtype: object\n"
          ]
        }
      ],
      "source": [
        "# check some random messages\n",
        "\n",
        "i=41\n",
        "print(message_df.message_tree_text.iloc[i])\n",
        "print('\\n')\n",
        "print(message_df.iloc[i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "BAKcwzAloBLC"
      },
      "outputs": [],
      "source": [
        "one_message_tree = df.query(f\"message_tree_id == '00df03d2-7e6b-4b98-a537-776567d10601'\").sort_values(\"created_date\")\n",
        "# one_message_tree\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "0r4cwmN3UtYa"
      },
      "outputs": [],
      "source": [
        "# check whole dataset\n",
        "\n",
        "# message_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b1gqwFxZUtbo",
        "outputId": "4c5135d4-3bb8-4d9f-f4a8-fe6bb6959f6d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['message_tree_id', 'message_tree_text'],\n",
              "    num_rows: 9823\n",
              "})"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# convert back to HF datasets format\n",
        "\n",
        "data = Dataset.from_pandas(message_df)\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88,
          "referenced_widgets": [
            "4ce878c2b50b4ce6bd6150db6f66c9bf",
            "c60a64e56fb5465086bd35167d190560",
            "152db01d048944b98a843310d0d8c981",
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            "3521e5ff15a543bab2c4e60e4da61f4e",
            "28a543d2a7f14e7bb498c0045e618bba",
            "f4f445a5122345baa694fc4fdd1e9fd0"
          ]
        },
        "id": "oaBHh5yWTkQm",
        "outputId": "917e0b70-6ce9-4270-8c28-3d9db9071b81"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4ce878c2b50b4ce6bd6150db6f66c9bf",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/9823 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['message_tree_id', 'message_tree_text', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "    num_rows: 9823\n",
              "})"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "data = data.map(lambda samples: tokenizer(samples[\"message_tree_text\"], padding=True, truncation=True,), batched=True)\n",
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "InpuNBeCx3hP"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "AQ_HCYruWIHU",
        "outputId": "f0e86f6a-bc2e-43e4-88a2-89f9372cc763"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "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",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:318: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
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              "      [586/614 5:56:05 < 17:04, 0.03 it/s, Epoch 0.95/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
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              "      [614/614 6:14:11, Epoch 1/1]\n",
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              "    <table border=\"1\" class=\"dataframe\">\n",
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              "      <td>450</td>\n",
              "      <td>1.294200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>475</td>\n",
              "      <td>1.266900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.286000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>525</td>\n",
              "      <td>1.252400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>550</td>\n",
              "      <td>1.281800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>575</td>\n",
              "      <td>1.299100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>1.298200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=614, training_loss=1.290572554746746, metrics={'train_runtime': 22488.4324, 'train_samples_per_second': 0.437, 'train_steps_per_second': 0.027, 'total_flos': 8.003914219624858e+17, 'train_loss': 1.290572554746746, 'epoch': 1.0})"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "training_args = transformers.TrainingArguments(\n",
        "    auto_find_batch_size=True,\n",
        "    gradient_accumulation_steps=4,\n",
        "    num_train_epochs=1,\n",
        "    learning_rate=2e-4,\n",
        "    fp16=True,\n",
        "    save_total_limit=4,\n",
        "    logging_steps=25,\n",
        "    output_dir=\"/content/drive/MyDrive/Colab Files/WM/falcon-chat-7b\",\n",
        "    save_strategy='epoch',\n",
        "    optim=\"paged_adamw_8bit\",\n",
        "    lr_scheduler_type = 'cosine',\n",
        "    warmup_ratio = 0.05,\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  # silence the warnings. Please re-enable for inference!\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Duak7T_B3VpJ"
      },
      "source": [
        "## Share adapters on the 🤗 Hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VxB6UV5XAvvP",
        "outputId": "c25bfe26-3e97-4983-f8a5-4544abd13f68"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/dfurman/falcon-7b-chat-oasst1/commit/c1d659b12ba143921a39039c5c73de8d08c915c8', commit_message='Upload model', commit_description='', oid='c1d659b12ba143921a39039c5c73de8d08c915c8', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.push_to_hub(\"dfurman/falcon-7b-chat-oasst1\", use_auth_token=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S65GcxNGA9kz"
      },
      "source": [
        "## Load adapters from the Hub\n",
        "\n",
        "You can also directly load adapters from the Hub using the commands below:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
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        },
        "id": "hsD1VKqeA62Z",
        "outputId": "ac11b44d-74e6-4281-980f-652a0d323a6a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8cbd584670c949b1ae0be34ba2624e9f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)/adapter_config.json:   0%|          | 0.00/333 [00:00<?, ?B/s]"
            ]
          },
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        },
        {
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            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
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        },
        {
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              "Downloading adapter_model.bin:   0%|          | 0.00/18.9M [00:00<?, ?B/s]"
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      ],
      "source": [
        "import torch\n",
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "peft_model_id = \"dfurman/falcon-7b-chat-oasst1\"\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    config.base_model_name_or_path, \n",
        "    return_dict=True, \n",
        "    load_in_8bit=True, \n",
        "    device_map={\"\":0},\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",
        "# Load the Lora model\n",
        "model = PeftModel.from_pretrained(model, peft_model_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MHYljmTjj5wX"
      },
      "source": [
        "## Inference\n",
        "\n",
        "You can then directly use the trained model or the model that you have loaded from the 🤗 Hub for inference as you would do it usually in `transformers`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "jlpAYdyVGoyo",
        "outputId": "4f9acc11-e3f0-4ba6-80b4-ddf74333d764"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'<human>: My name is Daniel. Write a short email to my closest friends inviting them to come to my home on Friday for a dinner party, I will make the food but tell them to BYOB. \\n<bot>:'"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "prompt = \"\"\"<human>: My name is Daniel. Write a short email to my closest friends inviting them to come to my home on Friday for a dinner party, I will make the food but tell them to BYOB. \n",
        "<bot>:\"\"\"\n",
        "\n",
        "prompt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5UpKIshHJQmm",
        "outputId": "e645a521-cc39-419d-fc91-8b8578ba6be4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[   39, 15564, 48190,  1814,  1536,   304,  8156,    25, 14687,   241,\n",
              "          1866,  2572,   271,   491, 14710,  2153, 19549,   612,   271,  1239,\n",
              "           271,   491,  1081,   313,  4201,   312,   241,  5947,  3054,    23,\n",
              "           295,   451,   717,   248,  1655,   480,  1705,   612,   271, 15528,\n",
              "         18791,    25,  4610,    39, 13359, 48190]], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n",
              "       device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]],\n",
              "       device='cuda:0')}"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "batch = tokenizer(\n",
        "    prompt,\n",
        "    padding=True,\n",
        "    truncation=True,\n",
        "    return_tensors='pt'\n",
        ")\n",
        "batch = batch.to('cuda:0')\n",
        "batch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MDqJWba-tpnv",
        "outputId": "c79f934f-d7dd-4258-abfd-c3ee85570530"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:318: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        }
      ],
      "source": [
        "with torch.cuda.amp.autocast():\n",
        "    output_tokens = model.generate(\n",
        "        input_ids = batch.input_ids, \n",
        "        max_new_tokens=200,\n",
        "        temperature=0.7,\n",
        "        top_p=0.7,\n",
        "        num_return_sequences=1,\n",
        "        pad_token_id=tokenizer.eos_token_id,\n",
        "        eos_token_id=tokenizer.eos_token_id,\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RCe7ly02JVis",
        "outputId": "7864d3b2-c875-41f9-c1cf-3cc0123c3c10"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dear friends,\n",
            "\n",
            "I am so excited to host a dinner party at my home this Friday! I will be making a delicious meal, but I would love for you to bring your favorite bottle of wine to share with everyone.\n",
            "\n",
            "Please let me know if you can make it and if you have any dietary restrictions I should be aware of. I look forward to seeing you soon!\n",
            "\n",
            "Best,\n",
            "Daniel\n",
            "\n"
          ]
        }
      ],
      "source": [
        "generated_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)\n",
        "# Inspect message response in the outputs\n",
        "print(generated_text.split(\"<human>: \")[1].split(\"<bot>: \")[-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-gRB13gmERkw"
      },
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