{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "5390a467",
      "metadata": {
        "id": "5390a467"
      },
      "source": [
        "# Лабораторная работа 1\n",
        "# Основы работы с большими языковыми моделями (LLM)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Установка необходимых библиотек**\n",
        "\n",
        "Перед началом работы необходимо установить библиотеки, которые используются для загрузки и запуска языковой модели.\n",
        "\n",
        "Мы устанавливаем:\n",
        "\n",
        "- **transformers** — библиотека Hugging Face для работы с готовыми языковыми моделями (LLM), токенизаторами и механизмами генерации текста.\n",
        "\n",
        "- **accelerate** — библиотека для эффективного запуска больших моделей:\n",
        "  - автоматическое распределение модели между CPU и GPU;\n",
        "  - оптимизация использования памяти;\n",
        "  - упрощение работы с большими нейронными сетями.\n",
        "\n",
        "- **torch (PyTorch)** — основной фреймворк для выполнения вычислений нейронной сети."
      ],
      "metadata": {
        "id": "bNz1lidU0Mhy"
      },
      "id": "bNz1lidU0Mhy"
    },
    {
      "cell_type": "code",
      "source": [
        "# Библиотека для работы с вычислениями на CPU/GPU\n",
        "import torch\n",
        "\n",
        "# Инструменты Hugging Face для загрузки моделей, токенизаторов и выполнения генерации текста\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "import pandas as pd\n",
        "import time\n",
        "\n",
        "#Большие языковые модели требуют значительных вычислительных ресурсов.\n",
        "#Перед запуском модели необходимо определить, где будут выполняться вычисления:\n",
        "\n",
        "# GPU (cuda) — видеокарта NVIDIA, которая позволяет значительно ускорить работу нейронных сетей;\n",
        "# CPU — обычный процессор компьютера, который используется, если GPU недоступна.\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "print(\"Используется устройство:\", device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "so6tbURo0rHp",
        "outputId": "cdc72315-7404-4432-be88-958986f4fba0"
      },
      "id": "so6tbURo0rHp",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Используется устройство: cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Загрузка языковой модели**\n",
        "\n",
        "В этом блоке выполняется загрузка большой языковой модели (LLM) и подготовка её к генерации текста.\n",
        "\n",
        "Мы используем модель **Qwen2.5-1.5B-Instruct** — инструкционную модель, которая была предварительно обучена на больших объёмах текстовых данных и дополнительно настроена для выполнения команд пользователя."
      ],
      "metadata": {
        "id": "nfL9pv9a1LIi"
      },
      "id": "nfL9pv9a1LIi"
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      },
      "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": [
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        {
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          "data": {
            "text/plain": [
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            "text/plain": [
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          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/7.03M [00:00<?, ?B/s]"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[transformers] `torch_dtype` is deprecated! Use `dtype` instead!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors: reconstructing file:   0%|          |  0.00B / 3.09GB            "
            ],
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              "model.safetensors: downloading bytes:           |  0.00B            "
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading weights:   0%|          | 0/338 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "05a0f18f65064a808f863bf80a369c11"
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        {
          "output_type": "display_data",
          "data": {
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            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "d8f5af0a74644efcb4e79e0ac8cedf9b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Модель загружена\n"
          ]
        }
      ],
      "source": [
        "model_name = \"Qwen/Qwen2.5-1.5B-Instruct\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    torch_dtype=torch.float16\n",
        ")\n",
        "\n",
        "model = model.to(device)\n",
        "print(\"Модель загружена\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "После загрузки модели проверим, что она корректно выполняет генерацию текста.\n",
        "Создадим простой запрос к модели и получим ответ."
      ],
      "metadata": {
        "id": "7e55ftQ134cv"
      },
      "id": "7e55ftQ134cv"
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"Расскажи про искусственный интеллект\"\n",
        "\n",
        "\n",
        "messages = [\n",
        "    {\n",
        "        \"role\": \"system\",\n",
        "        \"content\": \"Ты AI помощник Qwen.\"\n",
        "    },\n",
        "    {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": prompt\n",
        "    }\n",
        "]\n",
        "\n",
        "text = tokenizer.apply_chat_template(\n",
        "    messages,\n",
        "    tokenize=False,\n",
        "    add_generation_prompt=True\n",
        ")\n",
        "\n",
        "model_inputs = tokenizer(\n",
        "    [text],\n",
        "    return_tensors=\"pt\"\n",
        ").to(model.device)\n",
        "\n",
        "generated_ids = model.generate(\n",
        "    model_inputs.input_ids,\n",
        "    max_new_tokens=512\n",
        ")\n",
        "\n",
        "\n",
        "generated_ids = [\n",
        "    output_ids[len(input_ids):]\n",
        "    for input_ids, output_ids in zip(\n",
        "        model_inputs.input_ids,\n",
        "        generated_ids\n",
        "    )\n",
        "]\n",
        "\n",
        "\n",
        "response = tokenizer.batch_decode(\n",
        "    generated_ids,\n",
        "    skip_special_tokens=True\n",
        ")[0]\n",
        "\n",
        "\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bLgA5gZU399F",
        "outputId": "fb549beb-f63c-4beb-b6ba-792d7ae69e69"
      },
      "id": "bLgA5gZU399F",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Искусственный интеллект (AI) - это система, которая способна выполнять задачи, которые обычно выполняют человек. Он может обрабатывать большие объемы данных, анализировать информацию и принимать решения на основе этой информации.\n",
            "\n",
            "Основные характеристики искусственного интеллекта:\n",
            "\n",
            "1. Обучение: Источники данных используются для обучения алгоритмов и модели машинного обучения, чтобы они могли прогнозировать или решать задачи в будущем без дополнительного труда.\n",
            "\n",
            "2. Нейронные сети: Эти технологии позволяют создавать сложные алгоритмы, которые могут анализировать большие данные и находить связь между ними.\n",
            "\n",
            "3. Машинное обучение: Это метод обучения искусственных сущностей, который использует примеры из набора данных для определения законов, которые затем можно использовать для предсказания нового контекста.\n",
            "\n",
            "4. Распознавание образов: Включает в себя технологию распознавания лиц, объектов, языка и других формображений.\n",
            "\n",
            "5. Чат-боты: Это программирование, которое позволяет компьютеру общаться с людьми, как с человеком.\n",
            "\n",
            "6. Исследования: Используются для анализа научных исследований и разработки новых теорий.\n",
            "\n",
            "7. Погружение в окружающую среду: С помощью этого метода, системы могут \"погрузиться\" в реальную среду и получать информацию о том, что происходит вокруг них.\n",
            "\n",
            "8. Генерация текста: Используется для создания уникальной, грамматически правильной и полезной текстовой информации.\n",
            "\n",
            "9. Анализ данных: Для того чтобы понять и интерпретировать большие объемы данных.\n",
            "\n",
            "Важно отметить, что искусственный интеллект не заменяет человека, а помогает ему работать эффективнее и быстрее. Он предоставляет возможность делать вещи, которые невозможно сделать человеком, но также требует постоянного улучшения и совершенствования.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Далее добавим функцию, которая будет принимать модель, токенизатор, а также список сообщений пользователя и модели, и возвращать ответ модели в виде текста. Изучите, за что отвечают параметры генерации max_new_tokens; temperature=0.7; top_p, какие значения могут принимать и почему в коде выбраны именно эти значения."
      ],
      "metadata": {
        "id": "Ee3YVitjHjMV"
      },
      "id": "Ee3YVitjHjMV"
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_answer(\n",
        "    model,\n",
        "    tokenizer,\n",
        "    messages,\n",
        "    max_new_tokens=512,\n",
        "    temperature=0.7,\n",
        "    top_p=0.9\n",
        "):\n",
        "\n",
        "    text = tokenizer.apply_chat_template(\n",
        "        messages,\n",
        "        tokenize=False,\n",
        "        add_generation_prompt=True\n",
        "    )\n",
        "\n",
        "    model_inputs = tokenizer(\n",
        "        [text],\n",
        "        return_tensors=\"pt\"\n",
        "    ).to(model.device)\n",
        "\n",
        "\n",
        "    generated_ids = model.generate(\n",
        "        model_inputs.input_ids,\n",
        "        max_new_tokens=max_new_tokens,\n",
        "        temperature=temperature,\n",
        "        top_p=top_p,\n",
        "        do_sample=True\n",
        "    )\n",
        "\n",
        "\n",
        "    generated_ids = [\n",
        "        output_ids[len(input_ids):]\n",
        "        for input_ids, output_ids in zip(\n",
        "            model_inputs.input_ids,\n",
        "            generated_ids\n",
        "        )\n",
        "    ]\n",
        "\n",
        "\n",
        "    response = tokenizer.batch_decode(\n",
        "        generated_ids,\n",
        "        skip_special_tokens=True\n",
        "    )[0]\n",
        "\n",
        "\n",
        "    return response"
      ],
      "metadata": {
        "id": "r13m_mDbH0Zk"
      },
      "id": "r13m_mDbH0Zk",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "c06330f3",
      "metadata": {
        "id": "c06330f3"
      },
      "source": [
        "# 1. Базовый инференс модели\n",
        "\n",
        "## Задание 1\n",
        "\n",
        "Проверьте работу модели на разных типах задач:\n",
        "\n",
        "1. Фактический вопрос.\n",
        "2. Творческая генерация.\n",
        "3. Написание программного кода.\n",
        "\n",
        "Сделайте вывод о различиях качества ответов.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "00e976fb",
      "metadata": {
        "id": "00e976fb"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "85558391",
      "metadata": {
        "id": "85558391"
      },
      "source": [
        "# 2. Исследование параметров генерации\n",
        "\n",
        "## Задание 2\n",
        "\n",
        "Используйте запрос:\n",
        "\n",
        "> Придумай пять названий для стартапа в области искусственного интеллекта.\n",
        "\n",
        "Проверьте значения temperature:\n",
        "\n",
        "- 0.1\n",
        "- 0.5\n",
        "- 1.0\n",
        "- 1.5\n",
        "\n",
        "Сравните разнообразие ответов.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "89AjYz7cMgkk"
      },
      "id": "89AjYz7cMgkk",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "4507308a",
      "metadata": {
        "id": "4507308a"
      },
      "source": [
        "# 3. Prompt Engineering\n",
        "\n",
        "## Задание 3\n",
        "\n",
        "Сравните:\n",
        "\n",
        "1. простой запрос;\n",
        "2. запрос с ролью эксперта;\n",
        "3. запрос с ограничениями по формату ответа.\n",
        "\n",
        "Проанализируйте, как меняется качество результата.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iQX8YKyKMkK7"
      },
      "id": "iQX8YKyKMkK7",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "3cd44934",
      "metadata": {
        "id": "3cd44934"
      },
      "source": [
        "# 4. Суммаризация текста\n",
        "\n",
        "## Задание 4\n",
        "\n",
        "Используйте написанную выше функцию, чтобы получить пересказ рассказа \"Толстый и тонкий\" в 100 словах.\n",
        "\n",
        "Исследуйте, какие детали теряются при сокращении текста.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('Толстый и тонкий.txt') as f:\n",
        "    novel = f.read()\n",
        "\n",
        "print(novel)"
      ],
      "metadata": {
        "id": "GWvy6xk8M-nT"
      },
      "id": "GWvy6xk8M-nT",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nBOyuEGBN2FD"
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      "id": "nBOyuEGBN2FD",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "35bdc71d",
      "metadata": {
        "id": "35bdc71d"
      },
      "source": [
        "# 5. Ограничение контекстного окна\n",
        "\n",
        "## Задание 5\n",
        "\n",
        "Исследуйте влияние длины входного текста.\n",
        "\n",
        "Загрузите текст Капитанской дочки.\n",
        "Узнайте, сколько токенов в указанном произведении. Сравните это число с длиной контекста модели Qwen2. Может ли Qwen2 обработать это произведение за один запуск?\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('Капитанская дочка.txt') as f:\n",
        "    big_novel = f.read()\n",
        "\n",
        "print(big_novel[:200], \"\\n\\n\", \"*\"*50, \"\\n\\n\", big_novel[-200:])\n",
        "inputs = tokenizer(big_novel)\n",
        "print(\"Токенов в Капитанской дочке:\", len(inputs['input_ids']))\n",
        "print(\"Длина контекста: \", model.config.max_position_embeddings)"
      ],
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      },
      "id": "M_wU9BxDNGrb",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Напишите функцию, которая будет суммаризировать длинный текст, не помещающийся в контекст модели. Для этого длинный текст нужно разбить на короткие кусочки (chunks), каждый из которых можно уместить в контексте модели. Чтобы модели было проще уловить контекст, лучше не разбивать предложения. Для этого полезной может быть функция nltk.sent_tokenize(text), которая разбивает текст на предложения."
      ],
      "metadata": {
        "id": "Nfn5yIPkN4Dh"
      },
      "id": "Nfn5yIPkN4Dh"
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "id": "izgUN9B3N6LK"
      },
      "id": "izgUN9B3N6LK",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "2637db5b",
      "metadata": {
        "id": "2637db5b"
      },
      "source": [
        "# 6. Анализ галлюцинаций\n",
        "\n",
        "Создайте набор вопросов и оцените ошибки модели.\n"
      ]
    },
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      "cell_type": "code",
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      "metadata": {
        "id": "pOu8SqbDODkB"
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      "execution_count": null,
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