How to Train Your GPT is an educational textbook that teaches you to build a modern language model from scratch using Python and PyTorch.
The project addresses the gap between shallow API tutorials and dense academic papers by providing a fully annotated, beginner-friendly implementation of a transformer-based language model. It covers the complete pipeline: tokenization, embeddings, attention mechanisms, training loops, and inference. Every line of code includes comments explaining both what it does and why it exists. The architecture follows the LLaMA 3 style, and the material includes 28 standalone topic explainers covering techniques like rotary positional embeddings, RMSNorm, KV caching, and mixed precision training. Two narrative walkthroughs trace a single sentence through the entire model step by step.
This suits Python developers and students who want to understand transformer internals rather than just call APIs, with only basic Python knowledge required as a prerequisite. The project explicitly targets people who got stuck at the attention mechanism in other tutorials and those evaluating architectural tradeoffs. It is not a production framework but a learning resource designed to build deep understanding of how models like ChatGPT, Claude, and LLaMA actually work.
The project demonstrates sustained educational focus with comprehensive documentation across multiple chapters and topic explainers, all maintaining consistent pedagogical style. The codebase is fully annotated with explanatory comments throughout. The material includes both high-level analogies and rigorous technical detail, bridging conceptual understanding with mathematical foundations. The project is explicitly positioned as learning-focused rather than production-ready.
