how-to-train-your-gpt

Why we included this project

For developers who have only ever called an LLM API, this is a rare chance to actually see what happens inside the transformer. The guide walks through building a tokenizer, embeddings, attention, the training loop, and the inference engine yourself, with every line commented in plain language. Chapters are ordered so you don't need calculus or linear algebra, which makes it a practical entry point for Python programmers and students who gave up on academic papers. The 28 topic explainers cover things like RoPE versus learned positions and RMSNorm versus LayerNorm with worked examples, so you can compare design choices concretely. It's a learning project rather than production software, but the runnable code means you can tinker with a real model and see how changes affect training.