Implemented a full-stack web application for fine-tuning LLMs on email data, optimized for Apple Silicon (M4 Pro with 24GB RAM).
Features:
- Mail import with drag & drop support (.mbox, .eml, .txt)
- Automated mail cleaning and preprocessing
- Interactive labeling interface with keyboard shortcuts
- Training data export to JSONL format
- MLX-based LoRA fine-tuning with live updates
- Model evaluation and comparison interface
- Server-Sent Events for real-time training progress
- Dark theme UI optimized for extended use
Technical Stack:
- Backend: FastAPI with SQLite database
- Frontend: Vanilla HTML/CSS/JavaScript (no external dependencies)
- ML Framework: MLX for Apple Silicon optimization
- Models: Support for Mistral 7B and Llama 3 8B via MLX
Components:
- data_manager.py: SQLite operations for mail storage and labeling
- mail_parser.py: Parser for multiple mail formats with cleaning
- training.py: MLX training wrapper with LoRA support
- inference.py: Model loading and inference for evaluation
- main.py: FastAPI backend with REST API and SSE
- Frontend: Complete UI with all features
Documentation:
- Comprehensive README with installation and usage guide
- Quick-start guide for rapid setup
- Example mails for testing
- Troubleshooting and best practices
Ready for local deployment and fine-tuning workflows.
admin
Claude