LLM Model a game changer to the world of AI

LLM_model

This app is a dummy of the Big available LLM model using models like Ollama deepseek-r1:1.5b

🧠 DeepSeek Code Companion

Streamlit LangChain Ollama License: MIT

Your AI-powered pair programmer with advanced debugging capabilities and code optimization features.

Demo Screenshot

Features

  • 🚀 Multi-model support (DeepSeek, LLaVA, Llama3)
  • 🔥 Real-time code debugging assistance
  • 📝 Automatic code documentation generation
  • 💡 Intelligent solution design suggestions
  • 🎨 Streamlit-powered chat interface with dark theme
  • ⚙️ Customizable model parameters (temperature, model size)
  • 📚 Context-aware conversation history
  • 🖥️ Local LLM deployment via Ollama

Installation

  1. Prerequisites:

    • Ollama installed and running
    • Python 3.9+ environment

  2. Clone the repository: bash git clone https://github.com/yourusername/deepseek-code-companion.git cd deepseek-code-companion

  3. Install dependencies:

pip install -r requirements.txt
  1. Pull desired models (example for DeepSeek 1.5B):
ollama pull deepseek-r1:1.5b

Usage

  1. Start the Streamlit app:
streamlit run app.py

  1. Configure settings in the sidebar:

    • Select model variant (1.5B, 3B, 32B)
    • Adjust temperature for creativity control
    • View model capabilities

  2. Interact with the chat interface:

    • Type coding questions or paste error messages
    • Get AI-powered solutions with debugging support
    • Clear chat history as needed

Configuration

Available Models

Model Name Size Best For
deepseek-r1:1.5b 1.5B Quick answers, basic code
deepseek-r1:3b 3B Balanced performance
deepseek-r1:32b 32B Complex problem solving
llava:latest 7B Multimodal tasks
llama3.2:latest 70B Advanced reasoning

Temperature Guide

  • Low (0.0-0.3): Factual, deterministic responses
  • Medium (0.4-0.6): Balanced creativity
  • High (0.7-1.0): Creative solutions, experimental code

Technologies Used

  • Streamlit: Web interface and chat management
  • LangChain: LLM pipeline orchestration
  • Ollama: Local LLM deployment and management
  • DeepSeek Models: Specialized coding AI models
  • Custom CSS: Styled chat interface and components

Contributing

Contributions are welcome! Please follow these steps:

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add some amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Acknowledgements

  • Ollama team for seamless local LLM management
  • LangChain for LLM orchestration framework
  • DeepSeek for their specialized coding models
  • Streamlit for rapid UI development

Note: Ensure Ollama server is running at http://localhost:11434 before starting the app. Custom CSS styling can be modified in the app.py header section.