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Model: MTSAIR/Kodify-Nano Source: Original Platform
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Apache License MTS AI.docx
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Apache License MTS AI.docx
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README.md
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---
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library_name: transformers
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language:
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- ru
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- en
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pipeline_tag: text-generation
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license: apache-2.0
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license_name: apache-2.0
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license_link: https://huggingface.co/MTSAIR/Kodify-Nano/blob/main/Apache%20License%20MTS%20AI.docx
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---
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# Kodify-Nano 🤖
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Kodify-Nano – это легковесная LLM, разработанная для выполнения задач разработки кода с минимальными ресурсами. Она оптимизирована для быстрого и эффективного взаимодействия с пользователями, обеспечивая высокую производительность даже в условиях ограниченных ресурсов.
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Kodify-Nano is a lightweight LLM designed for code development tasks with minimal resource usage. It is optimized for fast and efficient interaction, delivering high performance even in resource-constrained environments.
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## Inference with vLLM
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```bash
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python3 -m vllm.entrypoints.openai.api_server --model MTSAIR/Kodify-Nano --port 8985
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```
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> **Important!** If you encounter the **"CUDA out of memory. Tried to allocate..."** error despite having sufficient GPU memory, try one of these solutions:
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> 1. Add the --enforce-eager argument
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> 2. Reduce GPU memory utilization (for example --gpu-memory-utilization 0.8)
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>
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> Note: This may decrease model performance.
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---
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## Using the Ollama Image
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You can run Kodify Nano on OLLAMA in two ways:
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1. **Using Docker**
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2. **Locally** (provides faster responses than Docker)
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### Method 1: Running Kodify Nano on OLLAMA in Docker
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#### Without NVIDIA GPU:
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```bash
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docker run -e OLLAMA_HOST=0.0.0.0:8985 -p 8985:8985 --name ollama -d ollama/ollama
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```
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#### With NVIDIA GPU:
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```bash
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docker run --runtime nvidia -e OLLAMA_HOST=0.0.0.0:8985 -p 8985:8985 --name ollama -d ollama/ollama
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```
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> **Important:**
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> - Ensure Docker is installed and running
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> - If port 8985 is occupied, replace it with any available port and update plugin configuration
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#### Load the model:
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```bash
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docker exec ollama ollama pull hf.co/MTSAIR/Kodify-Nano-GGUF
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```
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#### Rename the model:
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```bash
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docker exec ollama ollama cp hf.co/MTSAIR/Kodify-Nano-GGUF kodify_nano
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```
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#### Start the model:
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```bash
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docker exec ollama ollama run kodify_nano
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```
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---
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### Method 2: Local Kodify Nano on OLLAMA
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1. **Download OLLAMA:**
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https://ollama.com/download
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2. **Set the port:**
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```bash
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export OLLAMA_HOST=0.0.0.0:8985
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```
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> **Note:** If port 8985 is occupied, replace it and update plugin configuration
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3. **Start OLLAMA server:**
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```bash
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ollama serve &
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```
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4. **Download the model:**
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```bash
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ollama pull hf.co/MTSAIR/Kodify-Nano-GGUF
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```
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5. **Rename the model:**
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```bash
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ollama cp hf.co/MTSAIR/Kodify-Nano-GGUF kodify_nano
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```
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6. **Run the model:**
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```bash
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ollama run kodify_nano
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```
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## Plugin Installation
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### For Visual Studio Code
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1. Download the [latest Kodify plugin](https://mts.ai/ru/product/kodify/?utm_source=huggingface&utm_medium=pr&utm_campaign=post#models) for VS Code.
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2. Open the **Extensions** panel on the left sidebar.
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3. Click **Install from VSIX...** and select the downloaded plugin file.
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### For JetBrains IDEs
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1. Download the [latest Kodify plugin](https://mts.ai/ru/product/kodify/?utm_source=huggingface&utm_medium=pr&utm_campaign=post#models) for JetBrains.
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2. Open the IDE and go to **Settings > Plugins**.
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3. Click the gear icon (⚙️) and select **Install Plugin from Disk...**.
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4. Choose the downloaded plugin file.
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5. Restart the IDE when prompted.
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---
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### Changing the Port in Plugin Settings (for Visual Studio Code and JetBrains)
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If you changed the Docker port from `8985`, update the plugin's `config.json`:
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1. Open any file in the IDE.
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2. Open the Kodify sidebar:
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- **VS Code**: `Ctrl+L` (`Cmd+L` on Mac).
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- **JetBrains**: `Ctrl+J` (`Cmd+J` on Mac).
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3. Access the `config.json` file:
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- **Method 1**: Click **Open Settings** (VS Code) or **Kodify Config** (JetBrains), then navigate to **Configuration > Chat Settings > Open Config File**.
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- **Method 2**: Click the gear icon (⚙️) in the Kodify sidebar.
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4. Modify the `apiBase` port under `tabAutocompleteModel` and `models`.
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5. Save the file (`Ctrl+S` or **File > Save**).
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---
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### Example API Request
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```python
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import openai
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openai.api_key = 'xxx' # Replace with your key
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openai.api_base = 'http://localhost:8985/v1' # Update if port changed
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response = openai.ChatCompletion.create(
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model="MTSAIR/Kodify-Nano",
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temperature=0.4,
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max_tokens=2048,
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top_p=0.8,
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messages=[
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{"role": "user", "content": "Write a Python algorithm to find all unique words in a text."}
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]
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)
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print(response["choices"][0]["message"]["content"])
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### Inference with Hugging Face
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from transformers import pipeline
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pipe = pipeline("text-generation", model="MTSAIR/Kodify-Nano", device="cuda")
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messages = [
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{"role": "user", "content": "Write a Python algorithm to find all unique words in a text."},
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]
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response = pipe(messages, max_length=1024)
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print(response[0]['generated_text'][-1]['content'])
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```
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---
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## Example Interaction
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**User Request:**
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*Напиши простой сервер на flask с объяснениями на русском языке.*
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#### Kodify-Nano response:
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>Конечно! Давайте создадим простой сервер на Flask с объяснениями на русском языке. Flask - это мини-модуль для создания веб-приложений на Python, который позволяет быстро и легко разрабатывать приложения с использованием библиотеки Werkzeug.
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>### Установка Flask
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>Для начала, убедитесь, что у вас установлен Python. После этого установите Flask с помощью pip:
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||||
>```sh
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>pip install Flask
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>```
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>### Пример простого сервера на Flask
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>Вот пример простого приложения на Flask, которое будет отвечать на запросы на `/hello` и выводить "Hello, World!".
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>1. Создайте новый файл, например, `app.py`.
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>2. Откройте файл в текстовом редакторе и вставьте следующий код:
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|
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>```python
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>from flask import Flask
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||||
>app = Flask(__name__)
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>@app.route('/hello')
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>def hello_world():
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> return 'Hello, World!'
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>if __name__ == '__main__':
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> app.run(debug=True)
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>```
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>### Объяснение кода
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>1. **Импорт Flask**: Мы импортируем класс Flask из модуля `flask`.
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>2. **Создание экземпляра Flask**: Создаем экземпляр класса Flask с именем `app`.
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>3. **Обработчик запросов**: Мы декорируем функцию `hello_world` с помощью `@app.route('/hello')`. Это означает, что при запросе на `/hello` Flask вызовет функцию `hello_world`.
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>4. **Возвращение ответа**: Функция `hello_world` возвращает строку `'Hello, World!'`.
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>5. **Запуск сервера**: В конце файла добавляем проверку, чтобы при запуске скрипта он запускался в режиме отладки (`debug=True`).
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>### Запуск сервера
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>1. Откройте терминал и перейдите в директорию, в которой находится ваш файл `app.py`.
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>2. Запустите скрипт с помощью команды:
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||||
>```sh
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>python app.py
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>```
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>
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>3. Вы увидите сообщение, что сервер запущен на порту 5000. Например:
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>```
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> * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
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>```
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>4. Откройте браузер и перейдите на адрес `http://127.0.0.1:5000/hello`. Вы должны увидеть сообщение "Hello, World!".
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>### Дополнительные функции
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>Flask предоставляет множество других функций для создания более сложных приложений. Вот некоторые из них:
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>- **Функции маршрутизации**: Вы можете добавлять дополнительные маршруты, обрабатывать параметры запросов и т.д.
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>- **Вьюшки**: Вы можете создавать вьюшки, которые возвращают HTML-страницы.
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>- **Сессии**: Вы можете использовать сессии для хранения данных между запросами.
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>- **БД**: Вы можете подключаться к базе данных и выполнять запросы.
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>Давайте рассмотрим пример с вьюшкой:
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>```python
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>from flask import Flask, render_template
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>app = Flask(__name__)
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>@app.route('/')
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>def index():
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> return render_template('index.html')
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>if __name__ == '__main__':
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> app.run(debug=True)
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>```
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||||
>1. **Вьюшка**: Создаем файл `templates/index.html` в папке `templates` (если он еще не существует) и добавляем следующий код:
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||||
>
|
||||
>```html
|
||||
><!DOCTYPE html>
|
||||
><html lang="ru">
|
||||
><head>
|
||||
> <meta charset="UTF-8">
|
||||
> <title>Пример вьюшки</title>
|
||||
></head>
|
||||
><body>
|
||||
> <h1>Привет, мир!</h1>
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||||
> <p>Это пример вьюшки на Flask.</p>
|
||||
></body>
|
||||
></html>
|
||||
>```
|
||||
|
||||
>2. **Запуск сервера**: Сохраните файлы и запустите сервер снова. Откройте браузер и перейдите на адрес `http://127.0.0.1:5000/`. Вы должны увидеть HTML-страницу с текстом "Привет, мир!".
|
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added_tokens.json
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added_tokens.json
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{
|
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"</tool_call>": 151658,
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"<tool_call>": 151657,
|
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"<|box_end|>": 151649,
|
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"<|box_start|>": 151648,
|
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"<|endoftext|>": 151643,
|
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"<|file_sep|>": 151664,
|
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"<|fim_middle|>": 151660,
|
||||
"<|fim_pad|>": 151662,
|
||||
"<|fim_prefix|>": 151659,
|
||||
"<|fim_suffix|>": 151661,
|
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"<|im_end|>": 151645,
|
||||
"<|im_start|>": 151644,
|
||||
"<|image_pad|>": 151655,
|
||||
"<|object_ref_end|>": 151647,
|
||||
"<|object_ref_start|>": 151646,
|
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"<|quad_end|>": 151651,
|
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"<|quad_start|>": 151650,
|
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"<|repo_name|>": 151663,
|
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"<|video_pad|>": 151656,
|
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"<|vision_end|>": 151653,
|
||||
"<|vision_pad|>": 151654,
|
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"<|vision_start|>": 151652
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}
|
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config.json
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config.json
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{
|
||||
"architectures": [
|
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"Qwen2ForCausalLM"
|
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],
|
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"attention_dropout": 0.0,
|
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"bos_token_id": 151643,
|
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"eos_token_id": 151645,
|
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"hidden_act": "silu",
|
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"hidden_size": 1536,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 8960,
|
||||
"max_position_embeddings": 32768,
|
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"max_window_layers": 28,
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"model_type": "qwen2",
|
||||
"num_attention_heads": 12,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 2,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.46.1",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
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1
configuration.json
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configuration.json
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||||
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
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18
generation_config.json
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generation_config.json
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||||
{
|
||||
"transformers_version": "4.46.3",
|
||||
"temperature": 0.4,
|
||||
"max_len": 8192,
|
||||
"max_tokens": 4096,
|
||||
"do_sample": true,
|
||||
"presence_penalty": 0.0,
|
||||
"frequency_penalty": 0.0,
|
||||
"repetition_penalty": 1.0,
|
||||
"top_p": 0.8,
|
||||
"top_k": 20,
|
||||
"n": 1,
|
||||
"best_of": 1,
|
||||
"seed": null,
|
||||
"use_beam_search": false,
|
||||
"length_penalty": 1.0,
|
||||
"ignore_eos": false
|
||||
}
|
||||
3
merges.txt
Normal file
3
merges.txt
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:8831e4f1a044471340f7c0a83d7bd71306a5b867e95fd870f74d0c5308a904d5
|
||||
size 1671853
|
||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:6801c76a437735f49dcfd41b5b1b08546b370a2cea60b234c2dae0a2a477d348
|
||||
size 3087467144
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
|
||||
size 11421896
|
||||
210
tokenizer_config.json
Normal file
210
tokenizer_config.json
Normal file
@@ -0,0 +1,210 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Kodify, created by MTS AI, you are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Kodify, created by MTS AI. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"truncation_side": "left",
|
||||
"unk_token": null
|
||||
}
|
||||
BIN
vocab.json
(Stored with Git LFS)
Normal file
BIN
vocab.json
(Stored with Git LFS)
Normal file
Binary file not shown.
Reference in New Issue
Block a user