A project to build and train a Large Language Model (LLM) from scratch, implementing core components and training procedures to understand how modern language models work.
The final goal is to train a complete LLM from scratch, scaling to whatever size your hardware allows. This project focuses on understanding the fundamentals of transformer architectures, tokenization, training loops, and model optimization.
This project is a learning exercise to understand LLMs at a fundamental level. The implementation will prioritize clarity and educational value over optimization.
- Python 3.8+
- CUDA-capable GPU (recommended for training)
- Sufficient RAM/VRAM for your target model size
- Tokenizer implementation (BPE/WordPiece)
- Transformer architecture (attention, feed-forward, layer norm)
- Positional encoding
- Training loop with gradient accumulation
- Data loading and preprocessing pipeline
- Model checkpointing and resuming
- Inference engine
- Model quantization (for deployment)
- Attention Is All You Need - Original Transformer paper
- The Illustrated Transformer - Visual guide to transformers
- minGPT - Minimal GPT implementation reference