Active Context
Current Work Focus:
- ✅ Complete Hugging Face Space application with full model lifecycle management
- ✅ OpenAI-compatible API endpoints
- ✅ Environment-based configuration
Recent Changes:
- 2026-01-01: Complete project refactoring and feature implementation
- Created modular utils structure (model.py, chat_request.py, chat_response.py)
- Added download_model endpoint with automatic initialization
- Implemented startup event with .env configuration
- Added support for custom max_tokens from request
- Updated all memory bank documentation
Project Status: COMPLETE
Next Steps:
- Deploy to Hugging Face Spaces
- Test with real model downloads
- Monitor performance and optimize
Active Decisions and Considerations:
- ✅ Single model per instance (performance trade-off)
- ✅ Global state management for efficiency
- ✅ Environment configuration for flexibility
- ✅ OpenAI compatibility for ease of use
Important Patterns and Preferences:
- Modular architecture with clear separation of concerns
- Pydantic models for all request/response validation
- Comprehensive error handling with HTTP status codes
- Async handlers for concurrency
- Token counting with actual tokenizer
Learnings and Project Insights:
- Memory Bank is crucial for maintaining context across sessions
- Modular design makes testing and maintenance easier
- Environment variables provide deployment flexibility
- Startup events ensure ready-to-use application state
- Download + auto-initialize provides seamless user experience
Completed Features:
- ✅ FastAPI application with 3 endpoints
- ✅ Model download functionality
- ✅ Automatic model initialization on startup
- ✅ OpenAI-compatible chat completions
- ✅ Custom max_tokens support
- ✅ Environment-based configuration
- ✅ Modular utils architecture
- ✅ Comprehensive error handling
- ✅ Token counting with tokenizer
- ✅ Complete documentation in memory bank