model_fine-tuning-w-Qlora.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ggeorg0/rag-finetuned-mistral7b/blob/main/model_fine-tuning-w-Qlora.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sometimes Google Colab throws encding error. This statement used to it.\n",
        "import locale\n",
        "def getpreferredencoding(do_setlocale = True):\n",
        "    return \"UTF-8\"\n",
        "locale.getpreferredencoding = getpreferredencoding"
      ],
      "metadata": {
        "id": "iQiV_UcPgwzP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Установка и импорт зависимостей"
      ],
      "metadata": {
        "id": "h7Ikd2EggzxY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install auto-gptq\n",
        "!pip install optimum\n",
        "!pip install bitsandbytes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XeaOnjP2zsy-",
        "outputId": "14b59572-02df-4559-c656-c07f79cae00f"
      },
      "execution_count": null,
      "outputs": [
        {
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          "text": [
            "Collecting auto-gptq\n",
            "  Downloading auto_gptq-0.7.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (23.5 MB)\n",
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            "\u001b[?25hCollecting accelerate>=0.26.0 (from auto-gptq)\n",
            "  Downloading accelerate-0.28.0-py3-none-any.whl (290 kB)\n",
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            "\u001b[?25hCollecting datasets (from auto-gptq)\n",
            "  Downloading datasets-2.18.0-py3-none-any.whl (510 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m39.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from auto-gptq) (0.1.99)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from auto-gptq) (1.25.2)\n",
            "Collecting rouge (from auto-gptq)\n",
            "  Downloading rouge-1.0.1-py3-none-any.whl (13 kB)\n",
            "Collecting gekko (from auto-gptq)\n",
            "  Downloading gekko-1.1.0-py3-none-any.whl (13.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.2/13.2 MB\u001b[0m \u001b[31m85.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from auto-gptq) (2.2.1+cu121)\n",
            "Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from auto-gptq) (0.4.2)\n",
            "Requirement already satisfied: transformers>=4.31.0 in /usr/local/lib/python3.10/dist-packages (from auto-gptq) (4.38.2)\n",
            "Collecting peft>=0.5.0 (from auto-gptq)\n",
            "  Downloading peft-0.10.0-py3-none-any.whl (199 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.1/199.1 kB\u001b[0m \u001b[31m22.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->auto-gptq) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->auto-gptq) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.13.0->auto-gptq)\n",
            "  Downloading nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
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            "\u001b[?25hCollecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.13.0->auto-gptq)\n",
            "  Downloading nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
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            "\u001b[?25hCollecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.13.0->auto-gptq)\n",
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            "Installing collected packages: xxhash, rouge, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, gekko, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, nvidia-cusolver-cu12, datasets, accelerate, peft, auto-gptq\n",
            "Successfully installed accelerate-0.28.0 auto-gptq-0.7.1 datasets-2.18.0 dill-0.3.8 gekko-1.1.0 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 peft-0.10.0 rouge-1.0.1 xxhash-3.4.1\n",
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            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->optimum) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->optimum) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->optimum) (4.0.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.8.0->optimum) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.8.0->optimum) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.8.0->optimum) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.8.0->optimum) (2024.2.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.11->optimum) (2.1.5)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->optimum) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->optimum) (2023.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets->optimum) (1.16.0)\n",
            "Installing collected packages: humanfriendly, coloredlogs, optimum\n",
            "Successfully installed coloredlogs-15.0.1 humanfriendly-10.0 optimum-1.18.0\n",
            "Collecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.43.0-py3-none-manylinux_2_24_x86_64.whl (102.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m102.2/102.2 MB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from bitsandbytes) (2.2.1+cu121)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from bitsandbytes) (1.25.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (3.13.3)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (4.10.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (2023.6.0)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (2.19.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.105)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (2.2.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->bitsandbytes) (12.4.99)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->bitsandbytes) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->bitsandbytes) (1.3.0)\n",
            "Installing collected packages: bitsandbytes\n",
            "Successfully installed bitsandbytes-0.43.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# resolving \"No inf checks were recorded for this optimizer.\" issue\n",
        "!pip uninstall torch -y\n",
        "!pip install torch==2.1"
      ],
      "metadata": {
        "id": "ik7WwhkFftE0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c08e2fbb-eedd-4ad9-9d2e-318589535f5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing installation: torch 2.2.1+cu121\n",
            "Uninstalling torch-2.2.1+cu121:\n",
            "  Successfully uninstalled torch-2.2.1+cu121\n",
            "Collecting torch==2.1\n",
            "  Downloading torch-2.1.0-cp310-cp310-manylinux1_x86_64.whl (670.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m670.2/670.2 MB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch==2.1) (3.13.3)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch==2.1) (4.10.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch==2.1) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch==2.1) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch==2.1) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch==2.1) (2023.6.0)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch==2.1) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch==2.1) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch==2.1) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch==2.1) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch==2.1) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch==2.1) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch==2.1) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch==2.1) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch==2.1) (12.1.0.106)\n",
            "Collecting nvidia-nccl-cu12==2.18.1 (from torch==2.1)\n",
            "  Downloading nvidia_nccl_cu12-2.18.1-py3-none-manylinux1_x86_64.whl (209.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m209.8/209.8 MB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch==2.1) (12.1.105)\n",
            "Collecting triton==2.1.0 (from torch==2.1)\n",
            "  Downloading triton-2.1.0-0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (89.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.2/89.2 MB\u001b[0m \u001b[31m18.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.1) (12.4.99)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch==2.1) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch==2.1) (1.3.0)\n",
            "Installing collected packages: triton, nvidia-nccl-cu12, torch\n",
            "  Attempting uninstall: triton\n",
            "    Found existing installation: triton 2.2.0\n",
            "    Uninstalling triton-2.2.0:\n",
            "      Successfully uninstalled triton-2.2.0\n",
            "  Attempting uninstall: nvidia-nccl-cu12\n",
            "    Found existing installation: nvidia-nccl-cu12 2.19.3\n",
            "    Uninstalling nvidia-nccl-cu12-2.19.3:\n",
            "      Successfully uninstalled nvidia-nccl-cu12-2.19.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchaudio 2.2.1+cu121 requires torch==2.2.1, but you have torch 2.1.0 which is incompatible.\n",
            "torchtext 0.17.1 requires torch==2.2.1, but you have torch 2.1.0 which is incompatible.\n",
            "torchvision 0.17.1+cu121 requires torch==2.2.1, but you have torch 2.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed nvidia-nccl-cu12-2.18.1 torch-2.1.0 triton-2.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
        "from peft import prepare_model_for_kbit_training\n",
        "from peft import LoraConfig, get_peft_model\n",
        "from datasets import load_dataset\n",
        "import transformers"
      ],
      "metadata": {
        "id": "tfSl5xRs0y8J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load model"
      ],
      "metadata": {
        "id": "VVstnrh-0m2x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"TheBloke/Mistral-7B-Instruct-v0.2-GPTQ\"\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name,\n",
        "                                             device_map=\"auto\", # automatically figures out how to best use CPU + GPU for loading model\n",
        "                                             trust_remote_code=False, # prevents running custom model files on your machine\n",
        "                                             revision=\"gptq-4bit-32g-actorder_True\") # which version of model to use in repo"
      ],
      "metadata": {
        "id": "GFcKal6c96su",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "abc33b19-3410-485c-baff-08e63a9fbec9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:4193: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# test query\n",
        "user_query = \"Что делать, если потерял паспорт? Как сделать новый?\""
      ],
      "metadata": {
        "id": "ozq5jZruHPKT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Загрузка токенайзера модели"
      ],
      "metadata": {
        "id": "bCKC3_yl0pNS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ],
      "metadata": {
        "id": "c3CIa8C80Vtm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Проверка базовой модели"
      ],
      "metadata": {
        "id": "VnFiyBOc329l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.eval() # model in evaluation mode (dropout modules are deactivated)"
      ],
      "metadata": {
        "id": "g96qrZmcEZWJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Небольшой промт-инженеринг. Здесь задается шаблон, к которому будет прикрепляться сообщение пользователя. Сам пользователь не будет видеть этот текст"
      ],
      "metadata": {
        "id": "nR-GADyeQyqW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "intstructions_string = \"Вы - русскоязычный ИИ ассистент, который помогает пользователям находить файлы, \\\n",
        "отвечать на их вопросы и поддерживать диалог. \\\n",
        "Вы имеете доступ к базе данных, которая автоматически покажет результаты поиска, \\\n",
        "если найдется какая-либо информация. Помните, что не все результаты поиска могут быть релевантны. \\\n",
        "Адаптируйтесь к запросам пользователей и предоставляйте понятные и полезные ответы на вопросы, \\\n",
        "поддерживая диалог на русском языке. \\\n",
        "\\n\\\n",
        "\\n\\\n",
        "\\n\\\n",
        "Сообщение пользователя:\"\n",
        "\n",
        "def prompt_template(user_query):\n",
        "    return f'''[INST] {intstructions_string} \\n{user_query} \\n[/INST]'''\n",
        "\n",
        "\n",
        "prompt = prompt_template(user_query)\n",
        "print(prompt)"
      ],
      "metadata": {
        "id": "U77JPibZ4QuK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f10ca91-c046-4ed2-9c2c-a4b259ed69de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INST] Вы - русскоязычный ИИ ассистент, который помогает пользователям находить файлы, отвечать на их вопросы и поддерживать диалог. Вы имеете доступ к базе данных, которая автоматически покажет результаты поиска, если найдется какая-либо информация. Помните, что не все результаты поиска могут быть релевантны. Адаптируйтесь к запросам пользователей и предоставляйте понятные и полезные ответы на вопросы, поддерживая диалог на русском языке. \n",
            "\n",
            "\n",
            "Сообщение пользователя: \n",
            "Что делать, если потерял паспорт? Как сделать новый? \n",
            "[/INST]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# tokenize input\n",
        "inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
        "\n",
        "# generate output\n",
        "outputs = model.generate(input_ids=inputs[\"input_ids\"].to(\"cuda\"), max_new_tokens=280)\n",
        "\n",
        "print(tokenizer.batch_decode(outputs)[0])"
      ],
      "metadata": {
        "id": "EUaf1YyZ7R7G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f4104fe0-d96c-40f2-e89b-99f5aed6bd7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s> [INST] Вы - русскоязычный ИИ ассистент, который помогает пользователям находить файлы, отвечать на их вопросы и поддерживать диалог. Вы имеете доступ к базе данных, которая автоматически покажет результаты поиска, если найдется какая-либо информация. Помните, что не все результаты поиска могут быть релевантны. Адаптируйтесь к запросам пользователей и предоставляйте понятные и полезные ответы на вопросы, поддерживая диалог на русском языке. \n",
            "\n",
            "\n",
            "Сообщение пользователя: \n",
            "Что делать, если потерял паспорт? Как сделать новый? \n",
            "[/INST] При ло /******/тении паспорта необходимо сделать следующие шаги:\n",
            "\n",
            "1. Сообщить о потере документа в органе власти, который выдавал паспорт (ОВД, ФМС, МВД). Это необходимо для предотвращения возможных случаев фальсификации документа.\n",
            "2. Подготовить необходимые документы для получения нового паспорта:\n",
            "   - Для граждан Российской Федерации: свидетельство о рождении, свидетельство о браке (если женат), документы на детей (если есть), документы на жилье, личное письмо к органам власти с мотивированием потери паспорта.\n",
            "   - Для иностранцев: паспорт прежней страны проживания, свидетельство о рождении, свидетельство о браке (если женат), документы на детей (если есть), документы на жилье, личное письмо\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Как мы видим, модель сгенерировала какой-то странный резльутат, хотя и поняла основной смысл. Эту проблему можно решить с помощью файн-тьюнинга (дообучения) модели.**"
      ],
      "metadata": {
        "id": "PKK3IbFjhYAx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Подготовка датасета Yandex Q"
      ],
      "metadata": {
        "id": "w68GmVB7DX9o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"its5Q/yandex-q\", split=\"train\")\n",
        "dataset"
      ],
      "metadata": {
        "id": "mAYU7iTT40Vw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d6aec27-abe2-43c1-897e-a897b90dbc6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['description', 'question', 'answer'],\n",
              "    num_rows: 664246\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Отфильтруем данные по размеру ответа. А также оставим все вопросы, у которых есть описание, для того, чтобы в будущем использовать эту информацию в качестве \"поиска по базе данных\". Может быть это не совсем идеальный подход, но на мой взгляд он позволяет \"приучить\" модель к тому, что у неё есть доступ какой-то системе с дополнительной информацией."
      ],
      "metadata": {
        "id": "JVUnFTjAh37u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "low_threshould = 400\n",
        "high_threshould = 1600\n",
        "\n",
        "clean_dataset = dataset.filter(lambda ds: low_threshould <= len(ds['answer']) <= high_threshould\n",
        "                                          and len(ds['description']) > 100)"
      ],
      "metadata": {
        "id": "EjbPAxwtEdpt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clean_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_SBVjL9fFn39",
        "outputId": "4346dead-aa48-41ec-bc68-9d523ea97a9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['description', 'question', 'answer'],\n",
              "    num_rows: 43092\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Скачаем файлы \"плохих слов\" и популярных имен"
      ],
      "metadata": {
        "id": "rmllJ1RXPlQQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words/5faf2ba42d7b1c0977169ec3611df25a3c08eb13/ru\n",
        "!wget https://raw.githubusercontent.com/sorokinpf/russian_names/master/russian_names.txt\n",
        "!mv ru List-of-Bad-Words.txt\n",
        "!mv russian_names.txt popular_names.txt\n",
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5z2gTrYgIUyh",
        "outputId": "12e09c1d-a7a9-4b4b-99c1-c0fdd17783e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-03-31 09:35:40--  https://raw.githubusercontent.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words/5faf2ba42d7b1c0977169ec3611df25a3c08eb13/ru\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2223 (2.2K) [text/plain]\n",
            "Saving to: ‘ru’\n",
            "\n",
            "\rru                    0%[                    ]       0  --.-KB/s               \rru                  100%[===================>]   2.17K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-03-31 09:35:40 (56.9 MB/s) - ‘ru’ saved [2223/2223]\n",
            "\n",
            "--2024-03-31 09:35:40--  https://raw.githubusercontent.com/sorokinpf/russian_names/master/russian_names.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 61790 (60K) [text/plain]\n",
            "Saving to: ‘russian_names.txt’\n",
            "\n",
            "russian_names.txt   100%[===================>]  60.34K  --.-KB/s    in 0.008s  \n",
            "\n",
            "2024-03-31 09:35:40 (7.62 MB/s) - ‘russian_names.txt’ saved [61790/61790]\n",
            "\n",
            "List-of-Bad-Words.txt  popular_names.txt  qlora-mistral-hackatone-yandexq  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def read_file_with_words(filename):\n",
        "    words = []\n",
        "    with open(filename, 'r', encoding='UTF-8') as f:\n",
        "        for line in f:\n",
        "            words.append(line.strip())\n",
        "    return words\n",
        "\n",
        "bad_words = read_file_with_words('List-of-Bad-Words.txt')\n",
        "popular_names = read_file_with_words('popular_names.txt')"
      ],
      "metadata": {
        "id": "5r6lw_xhIYBi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def no_bad_words(text, restricted_words):\n",
        "    text = text.upper()\n",
        "    for rword in restricted_words:\n",
        "        if rword.upper() in text:\n",
        "            return False\n",
        "    return True\n",
        "\n",
        "# remove bad words\n",
        "clean_dataset = clean_dataset.filter(lambda ds: no_bad_words(ds['answer'], bad_words))"
      ],
      "metadata": {
        "id": "qevnQqhXL4KT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clean_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PSTTPK_Ojgta",
        "outputId": "761cedf6-7f0b-4155-e73c-8f934178ec1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['description', 'question', 'answer'],\n",
              "    num_rows: 33192\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Здесь вы можете видеть слова, которые, по моему мнению, встречаются в ответах низкого качества."
      ],
      "metadata": {
        "id": "B7uGLz-3PweO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "controversial_words = [\n",
        "    \"общение\", \"диалог\", \"разговор\", \"мотивация\",\n",
        "    \"стыд\", \"неловкость\", \"одиночество\", \"изоляция\",\n",
        "    \"отсутствие друзей\", \"социальная изоляция\", \"сомнения\",\n",
        "    \"ненависть к себе\", \"стресс\", \"тревожность\", \"низкое самооценка\",\n",
        "    \"отсутствие уверенности\", \"дети\", \"воспитание\", \"поведение\",\n",
        "    \"образование\", \"родители\", \"проблемы в школе\", \"забота о ребенке\",\n",
        "    \"отношения\", \"разрыв\", \"недоверие\", \"ревность\", \"изменяет\", \"одноклассники\",\n",
        "    \"партнер\", \"вторая половинка\", \"маму\", \"маме\", \"семье\", \"9 лет\"\n",
        "    \"10 лет\", \"11 лет\", \"12 лет\", \"13 лет\", \"14 лет\", \"15 лет\"\n",
        "]\n",
        "clean_dataset = clean_dataset.filter(lambda ds: no_bad_words(ds['answer'], controversial_words))\n",
        "clean_dataset = clean_dataset.filter(lambda ds: no_bad_words(ds['description'], controversial_words))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "fe3bfb7b90fe4e2b9a344f201bf3032b",
            "8c042265441f46d9b78a2edac5cb20fb",
            "5046aa114a5e4d738b19a37a85379f3f",
            "0b750fd003cb44b8b8420cb19d6c6aa2",
            "5cbf14fdd326409fb8c7efcd9793fdb6",
            "bb004dd3957546a08320f669a36e3b11",
            "402bbecaf5ad4146af74aaff01b410d1",
            "0a101cdf06f949b0b4f0722a1b94e2f1",
            "3218188760e94c35835f06a39338acd4",
            "8087e84ce997438091571cdfed06ccf1",
            "e283ad45775a471f9c3b1c45a0470f09",
            "21f9b0b8946d437da8b9d31100c8b8cc",
            "6b23802f8538487f84ee0f210283f7d1",
            "faad158bd004422da9a13e292b2af6e0",
            "8bd315eeb8264d2f88ada182b50115a4",
            "c212ff06f708429e98dc80b77c4a0de0",
            "ba26f8f345364a41ad94ca69a82a26c6",
            "a0558e9358fd4deba8a77a77a74a2c65",
            "722ad58828d54010a0c6ccecf9bffe00",
            "4d5a095a093a4d5c8a6dc41735a1e0db",
            "e27c537b1af546449b09726f9eff5e15",
            "80746fe2dece4f6884c9acd95cf260e1"
          ]
        },
        "id": "D5wNHfyAGUgX",
        "outputId": "7068af40-d633-4870-d368-f0ff50d69cd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/33192 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fe3bfb7b90fe4e2b9a344f201bf3032b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/25716 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "21f9b0b8946d437da8b9d31100c8b8cc"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clean_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "36VPpIYwGqB8",
        "outputId": "0e3fb7f0-2b84-4f16-e937-2648e9214a7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['description', 'question', 'answer'],\n",
              "    num_rows: 22830\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clean_dataset[19]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bD8_YS9laLQP",
        "outputId": "9cef007d-8e19-43df-c3af-d0cb3b31eeb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'description': 'С 1.01.2020 г. вроде как вступают в силу изменения по вывозу мусора. Соответственно все должны заключить договор с региональным оператором. И вроде как заключать договор лично не нужно так как он публичный и публикуется в местных СМИ. Отсюда вопрос с какого момента договор можно считать заключённым? Можно ли считать исполнение публичного договора если он опубликован в местной газете тиражом 5 тысяч копий, а в районе более 20 тысяч домовладений и более 110 тысяч жителей? Есть ли ответственность за не заключение договора? Откуда оператор будет знать норму на домовладение (раньше оплачивалось по количеству проживающих) и можно ли привлечь оператора или иное лицо предоставившее оператору личные данные для заключение договора (ФИО, количество проживающих и т.д. что может быть отображено в квитанциях)? В общем вопросов больше чем ответов.',\n",
              " 'question': 'С 1.01.2020 г. вроде как вступают в силу изменения по вывозу мусора. Вроде как все должны заключить договор с региональным оператором. ',\n",
              " 'answer': 'Заключается публичный договор оферты. Каким газета тиражём  совершенно не имеет значения, главное, что эта газета является тем печатным изданием которое предназначенно для публикации Постановлений, сообщений о проведении различных собраний, согласований. Относительно ваших персональных данных, наказание предусмотрено от административной до уголовной ,в зависимости от того  кто и что с ними сделал.'}"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Удалим привественные предложения, которые содержат имена. Пример: \"Здравствуйте, Семён!\""
      ],
      "metadata": {
        "id": "vJzia0wQQHHH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import string\n",
        "\n",
        "def remove_greetings(sample, names):\n",
        "    answer = sample['answer']\n",
        "    starting_words = answer.translate(str.maketrans('', '', string.punctuation)).split()\n",
        "    if any(n in starting_words for n in names):\n",
        "        greet_index = 50\n",
        "        for ch in '!.?,':\n",
        "            index = answer.find(ch, 0, 50)\n",
        "            if index != -1 and index < greet_index:\n",
        "                greet_index = index\n",
        "        if greet_index == 50:\n",
        "            return sample\n",
        "        # greet_index+2 to remove punktuation and whitespace\n",
        "        sample['answer'] = answer[greet_index+2:]\n",
        "    return sample\n",
        "\n",
        "# remove greetings\n",
        "clean_dataset = clean_dataset.map(lambda x: remove_greetings(x, popular_names), batched=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "966a96dc5e1b4c64b8e396c749e8d510",
            "6caefc61ab0e4703acfb14fcf1d08cae",
            "851ff233d9c74bc7972f7010680cfa1c",
            "4ecf74108b2443a6911a4a25e75cb01e",
            "758160bfd6f44e7b991a91e5097a6c2a",
            "fe6ecaf58a3a4daa876e4c1f1132996b",
            "1adb02043218473884c9f7d1a7bfa27f",
            "ff14247b04df4ceab5b344f32676e0e8",
            "51365c0ad84643c49aede4df72486489",
            "00d662178cb5450c9a2c3b4babc3bd9f",
            "2f89f6599ba542458583c5e6e7c2d914"
          ]
        },
        "id": "odeoZOerZvbt",
        "outputId": "3d547b78-65d7-4f34-b94e-1d2da8ba51e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/26769 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "966a96dc5e1b4c64b8e396c749e8d510"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clean_dataset[19]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0PqX9bx4JSrY",
        "outputId": "1545ecdf-31af-473a-d212-3761c4f7b608"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'description': 'С 1.01.2020 г. вроде как вступают в силу изменения по вывозу мусора. Соответственно все должны заключить договор с региональным оператором. И вроде как заключать договор лично не нужно так как он публичный и публикуется в местных СМИ. Отсюда вопрос с какого момента договор можно считать заключённым? Можно ли считать исполнение публичного договора если он опубликован в местной газете тиражом 5 тысяч копий, а в районе более 20 тысяч домовладений и более 110 тысяч жителей? Есть ли ответственность за не заключение договора? Откуда оператор будет знать норму на домовладение (раньше оплачивалось по количеству проживающих) и можно ли привлечь оператора или иное лицо предоставившее оператору личные данные для заключение договора (ФИО, количество проживающих и т.д. что может быть отображено в квитанциях)? В общем вопросов больше чем ответов.',\n",
              " 'question': 'С 1.01.2020 г. вроде как вступают в силу изменения по вывозу мусора. Вроде как все должны заключить договор с региональным оператором. ',\n",
              " 'answer': 'Заключается публичный договор оферты. Каким газета тиражём  совершенно не имеет значения, главное, что эта газета является тем печатным изданием которое предназначенно для публикации Постановлений, сообщений о проведении различных собраний, согласований. Относительно ваших персональных данных, наказание предусмотрено от административной до уголовной ,в зависимости от того  кто и что с ними сделал.'}"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Шаблон с которым мы будем обучать модель."
      ],
      "metadata": {
        "id": "CdKEEkrRQZgF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sample_template = \"\"\"<s>[INST]Вы - русскоязычный ИИ ассистент, который помогает пользователям находить файлы, \\\n",
        "отвечать на их вопросы и поддерживать диалог.\n",
        "\n",
        "\n",
        "дополнительная информация:\n",
        "{description}\n",
        "\n",
        "\n",
        "сообщение пользователя:\n",
        "{message}[/INST]{answer}\n",
        "<s>\n",
        "\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "mvpoLxSOKV5O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_and_tokenize(sample):\n",
        "    # extract text\n",
        "    text = sample_template.format(\n",
        "        description=sample[\"description\"],\n",
        "        message=sample[\"question\"],\n",
        "        answer=sample[\"answer\"]\n",
        "    )\n",
        "\n",
        "    #tokenize and truncate text\n",
        "    tokenizer.truncation_side = \"left\"\n",
        "    tokenized_inputs = tokenizer(\n",
        "        text,\n",
        "        return_tensors=\"np\",\n",
        "        truncation=True,\n",
        "        max_length=1024*2\n",
        "    )\n",
        "\n",
        "    return tokenized_inputs"
      ],
      "metadata": {
        "id": "nLdTufieJnGh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train, test = clean_dataset.train_test_split(test_size=0.1).values()\n",
        "print(f\"train: {len(train)}, test: {len(test)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6vU7iOUNP-7_",
        "outputId": "5c8e6049-590e-4caf-a122-23b5563c7f91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train: 24092, test: 2677\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = train.map(prepare_and_tokenize, batched=True, remove_columns=[\"description\", \"question\", \"answer\"])\n",
        "test = test.map(prepare_and_tokenize, batched=True, remove_columns=[\"description\", \"question\", \"answer\"])\n"
      ],
      "metadata": {
        "id": "D9LEA_g9gZPF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "66070f6df8054b89875ff7ab989d8453",
            "abc26a4ce7684743adc3b9033481920a",
            "387a95da229b4660bc45196722f44cfc",
            "fb80feae46bf47e8bf0086ed94f6b566",
            "6e640207050e4e788230960e9cdd3c82",
            "aa04295d4f7647a29f04e5fdc6b9d211",
            "9c875754d259456d89a8f1ecbe128839",
            "0f44fcbff0484b6a992f7896db753cf1",
            "b4b257a7259242d58a6c30a73c6a38f5",
            "53ee8c6540f84e8cac3bd4982491a73b",
            "9ab887e8eabe4e45870fb1690b6a30f7",
            "a8ecac8ca22244de97eb29e35588ac60",
            "dd2b92622f844e47b89c43cdc08b1c9c",
            "1bb3e7a8b91548d1bddee36d9f329519",
            "c4528a8d029a4f9d81f118255fabb2a9",
            "41b59d979f9f45f987e24480724fda36",
            "0d2d498419674452a95e2e58c0a97ed3",
            "9e969908029f4fac8fcf6fa2198b57d3",
            "81ae50ca555d4c1ebf077698e2d619c8",
            "fe9a69bd06654b1d8a4ba6c2ba72eb66",
            "4ca08a93ba6642fc9c50b8771569819f",
            "e2aabd8338d74a2b8b90060612fa5065"
          ]
        },
        "outputId": "28b7f917-a5d2-47fe-bb42-267c0b93138a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/24092 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "66070f6df8054b89875ff7ab989d8453"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2677 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a8ecac8ca22244de97eb29e35588ac60"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# setting pad token\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "# data collator\n",
        "data_collator = transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False)\n"
      ],
      "metadata": {
        "id": "pow8yLjKcJM8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prepare Model for Training"
      ],
      "metadata": {
        "id": "nXc8pBsVIy2N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.train() # model in training mode (dropout modules are activated)\n",
        "\n",
        "# enable gradient check pointing\n",
        "model.gradient_checkpointing_enable()\n",
        "\n",
        "# enable quantized training\n",
        "model = prepare_model_for_kbit_training(model)"
      ],
      "metadata": {
        "id": "K3PesDIyI1Hn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# LoRA config\n",
        "config = LoraConfig(\n",
        "    r=8,\n",
        "    lora_alpha=32,\n",
        "    target_modules=[\"q_proj\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "# LoRA trainable version of model\n",
        "model = get_peft_model(model, config)\n",
        "\n",
        "# trainable parameter count\n",
        "model.print_trainable_parameters()"
      ],
      "metadata": {
        "id": "iXOHXZcII5FS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6865a956-73e1-4359-bdec-d84a0316943b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 2,097,152 || all params: 264,507,392 || trainable%: 0.7928519441906561\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Fine-tuning Model"
      ],
      "metadata": {
        "id": "e-evbaTxhQTC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# hyperparameters\n",
        "lr = 5e-5\n",
        "batch_size = 8\n",
        "num_epochs = 60\n",
        "\n",
        "lora_model_name = \"qlora-mistral-hackatone-yandexq\"\n",
        "\n",
        "# define training arguments\n",
        "training_args = transformers.TrainingArguments(\n",
        "    output_dir=lora_model_name,\n",
        "    learning_rate=lr,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    num_train_epochs=num_epochs,\n",
        "    weight_decay=0.01,\n",
        "    logging_strategy=\"epoch\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    load_best_model_at_end=True,\n",
        "    gradient_accumulation_steps=4,\n",
        "    warmup_steps=2,\n",
        "    fp16=True,\n",
        "    optim=\"paged_adamw_8bit\",\n",
        "\n",
        ")"
      ],
      "metadata": {
        "id": "e1PPLr4McNii"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# configure trainer\n",
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=train,\n",
        "    eval_dataset=test,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator)\n",
        "\n",
        "\n",
        "# train model\n",
        "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!\n",
        "trainer.train()\n",
        "\n",
        "# renable warnings\n",
        "model.config.use_cache = True"
      ],
      "metadata": {
        "id": "m6wZK62bJKsJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "c9a9ea2b255a4f059886320228d917f5",
            "54c1f7bc638341e8ad31d1015675517b",
            "a734e953aee94b75b07779be6f5c5692",
            "787c23ae4b2b4e8eaa67ce90661afe19",
            "5711e288b7cd40719cc15df64872255c",
            "8533271202c44a35b0db57e8d023538c",
            "660a2d8292f045af9fca3d8a7efea5b5",
            "deed85ccde1441ddbcc4dfa04308ed08",
            "ccfcce7c001c43cfbb7b63f6350382d3",
            "4a3b35c920eb4c1891b7ab9af6616d5d",
            "a852525347a54ecc829b9e6c4e8c1f9c"
          ]
        },
        "outputId": "2526e15c-0502-4915-ae39-28fa0d1a72b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:432: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
            "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n",
            "  warnings.warn(\n",
            "/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='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [60/60 23:46, Epoch 60/60]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>2.016700</td>\n",
              "      <td>1.969901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.094900</td>\n",
              "      <td>1.968105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.070300</td>\n",
              "      <td>1.962376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.067400</td>\n",
              "      <td>1.956320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.005700</td>\n",
              "      <td>1.949980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.053400</td>\n",
              "      <td>1.943136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.991200</td>\n",
              "      <td>1.935859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>2.033300</td>\n",
              "      <td>1.928465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.993400</td>\n",
              "      <td>1.920955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>2.035800</td>\n",
              "      <td>1.913550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>1.972700</td>\n",
              "      <td>1.906368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>1.969800</td>\n",
              "      <td>1.899380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>1.998300</td>\n",
              "      <td>1.892811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>1.981000</td>\n",
              "      <td>1.886546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>1.955400</td>\n",
              "      <td>1.880692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>1.935000</td>\n",
              "      <td>1.875464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>1.920300</td>\n",
              "      <td>1.870513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>1.937100</td>\n",
              "      <td>1.866266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>1.918400</td>\n",
              "      <td>1.862463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.938000</td>\n",
              "      <td>1.859248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>1.940000</td>\n",
              "      <td>1.856490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>1.906200</td>\n",
              "      <td>1.854170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>1.929300</td>\n",
              "      <td>1.852015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>1.946400</td>\n",
              "      <td>1.850289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>1.927100</td>\n",
              "      <td>1.848791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>1.899800</td>\n",
              "      <td>1.847331</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>1.939300</td>\n",
              "      <td>1.846096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>1.918800</td>\n",
              "      <td>1.844897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>1.911700</td>\n",
              "      <td>1.843802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.897400</td>\n",
              "      <td>1.842770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>1.918100</td>\n",
              "      <td>1.841793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>1.904700</td>\n",
              "      <td>1.840924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>1.897700</td>\n",
              "      <td>1.839975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>1.893700</td>\n",
              "      <td>1.839226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>1.880100</td>\n",
              "      <td>1.838499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>1.914900</td>\n",
              "      <td>1.837735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>1.902700</td>\n",
              "      <td>1.837186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>1.907600</td>\n",
              "      <td>1.836631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>1.871800</td>\n",
              "      <td>1.836224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.912500</td>\n",
              "      <td>1.835741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>1.890300</td>\n",
              "      <td>1.835324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>1.866800</td>\n",
              "      <td>1.835010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>1.865300</td>\n",
              "      <td>1.834713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>1.906800</td>\n",
              "      <td>1.834452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>1.869000</td>\n",
              "      <td>1.834236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>1.884400</td>\n",
              "      <td>1.834013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>1.900100</td>\n",
              "      <td>1.833779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>1.886000</td>\n",
              "      <td>1.833631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>1.884700</td>\n",
              "      <td>1.833549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.856600</td>\n",
              "      <td>1.833335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>1.872900</td>\n",
              "      <td>1.833192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>1.873600</td>\n",
              "      <td>1.833034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>1.909800</td>\n",
              "      <td>1.832972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>1.897000</td>\n",
              "      <td>1.832869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>1.896600</td>\n",
              "      <td>1.832822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>1.894200</td>\n",
              "      <td>1.832765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>1.871000</td>\n",
              "      <td>1.832758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>1.843400</td>\n",
              "      <td>1.832668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>1.874300</td>\n",
              "      <td>1.832708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.847200</td>\n",
              "      <td>1.832666</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.08k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c9a9ea2b255a4f059886320228d917f5"
            }
          },
          "metadata": {}
        },
        {
          "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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Проверяем результаты"
      ],
      "metadata": {
        "id": "isqrTJynP-hL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JpLBG1p5QA2W",
        "outputId": "98d1a9d4-4af5-4d7f-dc29-f756b7bd4085"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INST] Вы - русскоязычный ИИ ассистент, который помогает пользователям находить файлы, отвечать на их вопросы и поддерживать диалог. Вы имеете доступ к базе данных, которая автоматически покажет результаты поиска, если найдется какая-либо информация. Помните, что не все результаты поиска могут быть релевантны. Адаптируйтесь к запросам пользователей и предоставляйте понятные и полезные ответы на вопросы, поддерживая диалог на русском языке. \n",
            "\n",
            "\n",
            "Сообщение пользователя: \n",
            "Кто такой Иван Бездомный? \n",
            "[/INST]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# tokenize input\n",
        "inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
        "\n",
        "# generate output\n",
        "outputs = model.generate(input_ids=inputs[\"input_ids\"].to(\"cuda\"), max_new_tokens=180)\n",
        "\n",
        "print(tokenizer.batch_decode(outputs)[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JcRsJvYxPxQz",
        "outputId": "44ddf53f-c8fe-4731-9c71-28968ac6e3f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s> [INST] Вы - русскоязычный ИИ ассистент, который помогает пользователям находить файлы, отвечать на их вопросы и поддерживать диалог. Вы имеете доступ к базе данных, которая автоматически покажет результаты поиска, если найдется какая-либо информация. Помните, что не все результаты поиска могут быть релевантны. Адаптируйтесь к запросам пользователей и предоставляйте понятные и полезные ответы на вопросы, поддерживая диалог на русском языке. \n",
            "\n",
            "\n",
            "Сообщение пользователя: \n",
            "Кто такой Иван Бездомный? \n",
            "[/INST] Иван Бездомный - это вымышленный персонаж из русской литературы. Он является одним из героев популярной русской сказки \"Морозко\" или \"Снежанка\" (в разных вариантах сказки его имя может звучать как Иван-да-Марьин, Иван-Царевич, Иван-Бездомный и т.д.). Этот персонаж известен своей доброй натурой, помогает главным героям сказки и спасает их от неприятностей. В сказке он часто одет в белые рубашки и живет в лесу. В некото\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Загрузка модели на Hugging Face"
      ],
      "metadata": {
        "id": "e3-3yynACoX0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()\n",
        "\n",
        "# # option 2: key login\n",
        "# from huggingface_hub import login\n",
        "# write_key = 'hf_' # paste token here\n",
        "# login(write_key)"
      ],
      "metadata": {
        "id": "_c5KhuhpCno-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "ba7b8b732e10422e944d541b1ced379b",
            "a776e554128d49fd868fe03eb125b7c7",
            "e08f2204c72b4b75ac07aa15783727db",
            "d74b321a0f91464b92c6c6d4d1e04da0",
            "b614ff92915644379341b096e15e9692",
            "20fbe02825344332a49a4000e089f65c",
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            "434d49c6a99845ca91cb7937935d5440",
            "54d30e234f1a4c3f9bb3341cfdc52668",
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            "788922028530465a83b6a3aa740c066d",
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            "59fcae384cc5407c88c47d2e66859a76",
            "38f468b21b4f4e26b7cd14c25ec3c792",
            "e334a7b4b54c45ada53413ff927e1466",
            "5bc7972d97f949949490367528b3f9d3",
            "c942937625684be59e5138d9a9d5cfb2",
            "199113d080e14c0c96847ee4c62db340",
            "2b1bd54d292340cebd58577c77eae020",
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            "6caa678407f744f8a6fc41b0f16f8761",
            "570bc2f2cfa9485196963dd46fec6047",
            "31e8cf7af20d4400b1dbaa0a185f82ba",
            "bae6dadb30364ff6a6297043414c190e",
            "ac8f953ea19e41c2b62214903ab939e0",
            "f3bcbf76d9d046d2a62b6480737597c4",
            "7eb864c55280412c9e7bdfca72a50780"
          ]
        },
        "outputId": "0df9135c-d6a0-4356-f080-26382cee13b5"
      },
      "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": "ba7b8b732e10422e944d541b1ced379b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hf_name = 'ggeorge' # your hf username or org name\n",
        "model_id = hf_name + \"/\" + lora_model_name"
      ],
      "metadata": {
        "id": "PYLbvvuPCq7q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(model_id)\n",
        "trainer.push_to_hub(model_id)"
      ],
      "metadata": {
        "id": "c4nAFjYPDC4_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180,
          "referenced_widgets": [
            "df82cac436fe4df1a9bcbc0e9d164840",
            "cbaeb78c64d844d4810fb28b6f9a8225",
            "2ea5b03cc708440abc552de53edf5f60",
            "01bae4c7993848c1a76205b0c5256233",
            "97b5b7a75319481c8f8325717f9eb870",
            "56bb64571dcb413693aec1c3e5e86573",
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            "e69e8898ca6542c381fabe95db2451eb",
            "fa1a4c8eed634bc9834827f5118d1d38",
            "5f3ee93b36584302b543bda6e8095495",
            "b689460dfb854ad19166d4d16ca5e651",
            "11ba750d710f4b75aeb4da29fac3d746",
            "e0644d61e4a644e8b37cd9ec440fdb16",
            "4f9994c1d13948a4bb2f41bf33bab389",
            "3ffe9370473e420d8226c086f2119547",
            "c675669276e243a2a0553389dda9b747",
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            "50d99e52675142cf8da5252b27d7cd10",
            "791010f4d7d44ca591574fd6f893cd70",
            "789056afea994c959eca8b6a5ee93cef",
            "4160f1fd83b845389f322b61ee5735e3",
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            "bee56885f9ff417abadb88516ac6c551",
            "b8f42aa113314b41967aa615ff74b8ec",
            "9259fee308d74db0ab14e179838ba5bb",
            "1a0974f72f0a44d2a9c8d363ffcf69c6",
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            "2417f891596f4b81a0ba60006e71cd4c",
            "3bb9eb827ed642cd95bf9e5a1d7ad40b",
            "168ac114face4a6089ac62fdba3b78a7",
            "9804e1be97ab46e484a167a0b60de083",
            "e8320ef564eb492a8c0df6788f590f3b",
            "5c3d09a9d63e4aaeb774dfbc6a0fc293",
            "ddb62750b2b54d4696967045400e85bf",
            "c2c910e799d24bf2b300b56a49810ba6",
            "944ea228762a4894a18880c1230b4891",
            "18fa72d52e3a438a821d9459f9cd8695",
            "39473fa69d844ac98924ad4680ba31a2"
          ]
        },
        "outputId": "f41b9b45-a1fb-4b19-cc83-2e356d114d65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/8.40M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "df82cac436fe4df1a9bcbc0e9d164840"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "training_args.bin:   0%|          | 0.00/4.92k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "232ca307cc334824b2ad6e8f3dafbf83"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "events.out.tfevents.1711874118.8413cff6e703.688.0:   0%|          | 0.00/34.0k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d19aa53575124ffd92c6d25a5a939a4d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2417f891596f4b81a0ba60006e71cd4c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/ggeorge/qlora-mistral-hackatone-yandexq/commit/e495d14229592d48b258241105e6d843cf71e24f', commit_message='ggeorge/qlora-mistral-hackatone-yandexq', commit_description='', oid='e495d14229592d48b258241105e6d843cf71e24f', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Работа с fine-tuned моделью"
      ],
      "metadata": {
        "id": "dDxRMA_BCBAt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load model from hub\n",
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForCausalLM\n",
        "\n",
        "model_name = \"TheBloke/Mistral-7B-Instruct-v0.2-GPTQ\"\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name,\n",
        "                                             device_map=\"auto\",\n",
        "                                             trust_remote_code=False,\n",
        "                                             revision=\"gptq-4bit-32g-actorder_True\")\n",
        "\n",
        "config = PeftConfig.from_pretrained(\"ggeorge/qlora-mistral-hackatone-yandexq\")\n",
        "model = PeftModel.from_pretrained(model, \"ggeorge/qlora-mistral-hackatone-yandexq\")\n",
        "\n",
        "# load tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)"
      ],
      "metadata": {
        "id": "T9gRgNjNiTvK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = prompt_template('Что делать, если потерял паспорт? Как сделать новый?')\n",
        "print(prompt)"
      ],
      "metadata": {
        "id": "ITUJZDIYjs4t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb61f787-d733-4678-e605-5834de4e1036"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INST] Вы - русскоязычный ИИ ассистент, который помогает пользователям находить файлы, отвечать на их вопросы и поддерживать диалог. Вы имеете доступ к базе данных, которая автоматически покажет результаты поиска, если найдется какая-либо информация. Помните, что не все результаты поиска могут быть релевантны. Адаптируйтесь к запросам пользователей и предоставляйте понятные и полезные ответы на вопросы, поддерживая диалог на русском языке. \n",
            "\n",
            "\n",
            "Сообщение пользователя: \n",
            "Что делать, если потерял паспорт? Как сделать новый? \n",
            "[/INST]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.eval()\n",
        "\n",
        "inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
        "outputs = model.generate(input_ids=inputs[\"input_ids\"].to(\"cuda\"), max_new_tokens=280)\n",
        "\n",
        "print(tokenizer.batch_decode(outputs)[0])"
      ],
      "metadata": {
        "id": "j9wNZ2URivBW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da617b40-c9e0-4ef2-9d3f-581b8486c64a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s> [INST] Вы - русскоязычный ИИ ассистент, который помогает пользователям находить файлы, отвечать на их вопросы и поддерживать диалог. Вы имеете доступ к базе данных, которая автоматически покажет результаты поиска, если найдется какая-либо информация. Помните, что не все результаты поиска могут быть релевантны. Адаптируйтесь к запросам пользователей и предоставляйте понятные и полезные ответы на вопросы, поддерживая диалог на русском языке. \n",
            "\n",
            "\n",
            "Сообщение пользователя: \n",
            "Что делать, если потерял паспорт? Как сделать новый? \n",
            "[/INST] При потере паспорта необходимо сделать следующие шаги:\n",
            "\n",
            "1. Сообщить о потере документа в органе внутренних дел. Для этого необходимо заполнить соответствующую форму и принести с собой документы, подтверждающие личность (например, водительские права, свидетельство о рождении, свидетельство о браке, свидетельство о регистрации жительства).\n",
            "2. Заплатить плату за выдачу нового паспорта.\n",
            "3. Получить временное удостоверение личности (в случае потери всех документов).\n",
            "4. Получить новый паспорт.\n",
            "\n",
            "Для получения нового паспорта необходимо собрать необходимые документы и зайти в органы внутренних дел. В зависимости от региона могут быть различные требования к документам, поэтому рекомендуется позвонить в органы\n"
          ]
        }
      ]
    }
  ]
}