LLM Project Documentation
Welcome to the LLM project documentation. This index provides quick navigation to all available guides and references.
Quick Links
| Document | Description |
|---|---|
| Usage Guide | Training, inference, and serving instructions |
| Architecture | System design and component overview |
| Tutorial | Step-by-step CPU LLM training tutorial |
| Development | Setting up the development environment |
| FAQ | Frequently asked questions |
| Troubleshooting | Common issues and solutions |
Guides
In-depth guides for specific features:
| Guide | Description |
|---|---|
| Fine-Tuning (LoRA/QLoRA) | Parameter-efficient fine-tuning methods |
| Inference Optimization | KVCache, GQA, sliding window attention |
Training Framework
Detailed documentation for the training system:
| Document | Description |
|---|---|
| Overview | Training framework introduction |
| Components | Core training components |
| Training Flow | End-to-end training process |
| Callbacks | Callback system for extensibility |
| Configuration | Configuration guide |
| Extending | How to extend the framework |
| DDP Deep Dive | Distributed training details |
| Troubleshooting | Training-specific issues |
Architecture Decision Records (ADR)
Design decisions and their rationale:
| ADR | Topic |
|---|---|
| 001 | Grouped Query Attention (GQA) |
| 002 | SwiGLU Activation |
| 003 | Using prek for Git hooks |
| 004 | Using ty for type checking |
Deep Dives
In-depth technical explorations:
Contributing: See CONTRIBUTING.md for contribution guidelines.
Issues: Report bugs at GitHub Issues.