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

LLM_model

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

🧠 DeepSeek Code Companion

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

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
   

4. Pull desired models (example for DeepSeek 1.5B):

   ollama pull deepseek-r1:1.5b
   

Usage

1. Start the Streamlit app:

   streamlit run app.py
   

2. Configure settings in the sidebar:
   • Select model variant (1.5B, 3B, 32B)
   • Adjust temperature for creativity control
   • View model capabilities
3. 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

LLM Models

Model Name: `deepseek-r1:1.5b`
Size: 1.5B
Best For: Quick answers, basic code

Model Name: `deepseek-r1:3b`
Size: 3B
Best For: Balanced performance

Model Name: `deepseek-r1:32b`
Size: 32B
Best For: Complex problem solving

Model Name: `llava:latest`
Size: 7B
Best For: Multimodal tasks

Model Name: `llama3.2:latest`
Size: 70B
Best For: 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

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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.