Created a complete educational resource covering the implementation of
nanochat from scratch, including:
- Mathematical foundations (linear algebra, optimization, attention)
- Tokenization with detailed BPE algorithm explanation
- Transformer architecture and GPT model implementation
- Self-attention mechanism with RoPE and Multi-Query Attention
- Training process, data loading, and distributed training
- Advanced optimization techniques (Muon + AdamW)
- Practical implementation guide with debugging tips
- Automated PDF compilation script
The guide includes deep code walkthroughs with line-by-line explanations
of key components, making it accessible for beginners while covering
advanced techniques used in modern LLMs.
Total content: ~4,300 lines across 8 chapters plus README and tooling.
PDF compilation available via compile_to_pdf.py script.
