{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "989dd6a6",
      "metadata": {
        "id": "989dd6a6"
      },
      "source": [
        "# Лабораторная работа 2\n",
        "# Продвинутое использование больших языковых моделей (LLM)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3828242e",
      "metadata": {
        "id": "3828242e"
      },
      "source": [
        "# 1. Квантизация больших языковых моделей\n",
        "\n",
        "Большие модели требуют значительных вычислительных ресурсов.\n",
        "Квантизация позволяет уменьшить размер модели за счет использования чисел меньшей разрядности.\n",
        "\n",
        "В этой части исследуется:\n",
        "\n",
        "- FP16/BF16 представление;\n",
        "- INT8;\n",
        "- INT4;\n",
        "- влияние квантизации на качество ответа.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "6b2e535a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6b2e535a",
        "outputId": "124bb03c-240a-4161-8ffa-ad7e0ab3dbed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.50.2-py3-none-manylinux_2_24_x86_64.whl.metadata (10 kB)\n",
            "Requirement already satisfied: torch<3,>=2.4 in /usr/local/lib/python3.13/dist-packages (from bitsandbytes) (2.11.0+cu128)\n",
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            "Requirement already satisfied: cuda-bindings<13,>=12.9.4 in /usr/local/lib/python3.13/dist-packages (from torch<3,>=2.4->bitsandbytes) (12.9.7)\n",
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            "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.13/dist-packages (from torch<3,>=2.4->bitsandbytes) (0.7.1)\n",
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            "Requirement already satisfied: nvidia-nvshmem-cu12==3.4.5 in /usr/local/lib/python3.13/dist-packages (from torch<3,>=2.4->bitsandbytes) (3.4.5)\n",
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            "Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1.* in /usr/local/lib/python3.13/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch<3,>=2.4->bitsandbytes) (12.8.4.1)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90.* in /usr/local/lib/python3.13/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch<3,>=2.4->bitsandbytes) (12.8.90)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83.* in /usr/local/lib/python3.13/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch<3,>=2.4->bitsandbytes) (11.3.3.83)\n",
            "Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3.* in /usr/local/lib/python3.13/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch<3,>=2.4->bitsandbytes) (1.13.1.3)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90.* in /usr/local/lib/python3.13/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch<3,>=2.4->bitsandbytes) (12.8.90)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.9.90.* in /usr/local/lib/python3.13/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch<3,>=2.4->bitsandbytes) (10.3.9.90)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90.* in /usr/local/lib/python3.13/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch<3,>=2.4->bitsandbytes) (11.7.3.90)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93.* in /usr/local/lib/python3.13/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch<3,>=2.4->bitsandbytes) (12.5.8.93)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93.* in /usr/local/lib/python3.13/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch<3,>=2.4->bitsandbytes) (12.8.93)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93.* in /usr/local/lib/python3.13/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch<3,>=2.4->bitsandbytes) (12.8.93)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.8.90.* in /usr/local/lib/python3.13/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch<3,>=2.4->bitsandbytes) (12.8.90)\n",
            "Requirement already satisfied: cuda-pathfinder~=1.1 in /usr/local/lib/python3.13/dist-packages (from cuda-bindings<13,>=12.9.4->torch<3,>=2.4->bitsandbytes) (1.7.0)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.13/dist-packages (from sympy>=1.13.3->torch<3,>=2.4->bitsandbytes) (1.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.13/dist-packages (from jinja2->torch<3,>=2.4->bitsandbytes) (3.0.3)\n",
            "Downloading bitsandbytes-0.50.2-py3-none-manylinux_2_24_x86_64.whl (43.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.1/43.1 MB\u001b[0m \u001b[31m17.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: bitsandbytes\n",
            "Successfully installed bitsandbytes-0.50.2\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.13/dist-packages (2.2.3)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.13/dist-packages (1.6.1)\n",
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          ]
        }
      ],
      "source": [
        "%pip install -U bitsandbytes\n",
        "%pip install pandas scikit-learn"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1f32d51b",
      "metadata": {
        "id": "1f32d51b"
      },
      "source": [
        "## Задание 1.1\n",
        "\n",
        "Загрузите модель:\n",
        "\n",
        "- Qwen2.5-7B-Instruct (или аналогичную большую модель)\n",
        "\n",
        "с использованием 4-bit квантизации.\n",
        "\n",
        "Ответьте:\n",
        "\n",
        "1. Почему большая модель может не помещаться в GPU?\n",
        "2. Как изменяется объем памяти при переходе FP16 → INT8 → INT4?\n",
        "3. Какие потери качества могут возникнуть?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "74b05399",
      "metadata": {
        "colab": {
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          "height": 423,
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        },
        "id": "74b05399",
        "outputId": "336fc6f4-98af-420b-a914-e173663ad2a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
            "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/663 [00:00<?, ?B/s]"
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        {
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          "name": "stdout",
          "text": [
            "Модель загружена\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n",
        "import torch\n",
        "\n",
        "model_name = \"Qwen/Qwen2.5-7B-Instruct\"\n",
        "\n",
        "quant_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    quantization_config=quant_config,\n",
        "    device_map=\"auto\"\n",
        ")\n",
        "\n",
        "print(\"Модель загружена\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d9c1142d",
      "metadata": {
        "id": "d9c1142d"
      },
      "source": [
        "# 1.2 Сравнение моделей\n",
        "\n",
        "## Задание\n",
        "\n",
        "Сравните:\n",
        "\n",
        "- маленькую модель без квантизации;\n",
        "- большую модель с INT4 квантизацией.\n",
        "\n",
        "Оцените:\n",
        "\n",
        "- скорость генерации;\n",
        "- потребление памяти;\n",
        "- качество ответа.\n",
        "\n",
        "Используйте одинаковые запросы.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "20ffc892",
      "metadata": {
        "id": "20ffc892"
      },
      "outputs": [],
      "source": [
        "import time\n",
        "\n",
        "prompt = \"Explain how neural networks learn.\"\n",
        "\n",
        "start = time.time()\n",
        "\n",
        "# Здесь разместите вызов модели\n",
        "\n",
        "end = time.time()\n",
        "\n",
        "print(\"Время генерации:\", end-start)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8d0533f3",
      "metadata": {
        "id": "8d0533f3"
      },
      "source": [
        "# 2. Оценка способности LLM решать задачи\n",
        "\n",
        "Большие языковые модели применяются не только для генерации текста,\n",
        "но и для решения задач с проверяемым ответом.\n",
        "\n",
        "В этой части используется датасет математических задач. Для удобства валидации ответов конвертируем строковые ответы в float\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3a2adb91",
      "metadata": {
        "id": "3a2adb91"
      },
      "outputs": [],
      "source": [
        "def convert_to_float(frac_str):\n",
        "    try:\n",
        "        return float(frac_str)\n",
        "    except ValueError:\n",
        "        num, denom = frac_str.split('/')\n",
        "        try:\n",
        "            leading, num = num.split(' ')\n",
        "            whole = float(leading)\n",
        "        except ValueError:\n",
        "            whole = 0\n",
        "        frac = float(num) / float(denom)\n",
        "        return whole - frac if whole < 0 else whole + frac"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(\"./algebra__linear_1d_test.csv\")\n",
        "df = df.sample(50)  # выберем случайные 50 задач\n",
        "df.answer = df.answer.apply(lambda x: convert_to_float(x[2:-3]))  # представим данные в удобном для нас виде\n",
        "df.question = df.question.apply(lambda x: x[2:-3])\n",
        "df"
      ],
      "metadata": {
        "id": "7vj_OnUdjqTc"
      },
      "id": "7vj_OnUdjqTc",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Как и в прошлой работе, загрузите модель в память. Используйте полученные в прошлой лабораторной функции"
      ],
      "metadata": {
        "id": "cc23VPY5jw-d"
      },
      "id": "cc23VPY5jw-d"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IzQbPu_8juAE"
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      "id": "IzQbPu_8juAE",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "69bc8e3e",
      "metadata": {
        "id": "69bc8e3e"
      },
      "source": [
        "# 2.1 Решение без рассуждений\n",
        "\n",
        "## Задание\n",
        "\n",
        "Попросите модель вернуть только числовой ответ.\n",
        "\n",
        "Пример промпта:\n",
        "\n",
        "```\n",
        "Solve the following mathematical problem.\n",
        "Return only the answer.\n",
        "```\n",
        "\n",
        "После получения ответов вычислите:\n",
        "\n",
        "- Accuracy;\n",
        "- MAE (Mean Absolute Error).\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "def extract_first_number_from_string(text):\n",
        "    return float(re.findall(\"[-+]?[.]?[\\d]+(?:,\\d\\d\\d)*[\\.]?\\d*(?:[eE][-+]?\\d+)?\", text)[-1])"
      ],
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      "execution_count": null,
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    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-kIIXNSsikiH"
      },
      "id": "-kIIXNSsikiH",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "9e345089",
      "metadata": {
        "id": "9e345089"
      },
      "source": [
        "# 2.2 Решение с reasoning\n",
        "\n",
        "Теперь разрешите модели использовать рассуждение:\n",
        "\n",
        "```\n",
        "Solve the problem step by step.\n",
        "At the end write:\n",
        "Answer: number\n",
        "```\n",
        "\n",
        "Сравните результаты:\n",
        "\n",
        "- изменилось ли качество?\n",
        "- увеличилось ли время ответа?\n",
        "- стало ли легче анализировать ошибки?\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "m8AwTPepiz80"
      },
      "id": "m8AwTPepiz80",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "170051dd",
      "metadata": {
        "id": "170051dd"
      },
      "source": [
        "# 3. Анализ ошибок модели\n",
        "\n",
        "## Задание\n",
        "\n",
        "Выберите минимум 10 задач, где модель ошиблась.\n",
        "\n",
        "Для каждой ошибки определите тип:\n",
        "\n",
        "- ошибка вычисления;\n",
        "- неправильное понимание условия;\n",
        "- ошибка формата ответа;\n",
        "- галлюцинация;\n",
        "- недостаток рассуждения.\n",
        "\n",
        "Сделайте выводы.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZgtSvU5BizPc"
      },
      "id": "ZgtSvU5BizPc",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "49e96922",
      "metadata": {
        "id": "49e96922"
      },
      "source": [
        "# 4. Исследование reasoning\n",
        "\n",
        "Проверьте влияние различных стратегий:\n",
        "\n",
        "- без reasoning;\n",
        "- step-by-step;\n",
        "- роль эксперта;\n",
        "- few-shot примеры.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "plXmumtWiyt8"
      },
      "id": "plXmumtWiyt8",
      "execution_count": null,
      "outputs": []
    }
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