update
May 9, 2026
By Teun
How-to-Train-Your-GPT teaches modern LLMs from scratch
A new open source guide called How-to-Train-Your-GPT walks readers through building, training, and running a language model from scratch. The 12-chapter, 3,900-line interactive textbook focuses on modern decoder-only Transformer parts such as attention, RoPE, RMSNorm, and KV cache.
A new open source project called How-to-Train-Your-GPT aims to teach people how modern language models work by having them build one from scratch. The author describes it as a 12-chapter, 3,900-plus-line interactive textbook that covers both the concepts and the code behind a GPT-style model.
According to the project README, the guide is meant for readers who know basic Python but do not need prior machine learning experience. It starts with broad explanations and simple analogies, then moves into worked examples, annotated code, and diagrams.
⚡ New to this?
This is a new educational project that teaches how a GPT-style language model is built, piece by piece. GPT stands for Generative Pre-trained Transformer, and a Transformer is the neural network design behind many chatbots and text tools.
For a non-expert, the main value is that it turns a black box into something readable and runnable. That matters because language models are now used in products, automation, and security tools, and understanding the basics helps people judge what these systems can and cannot do.
🦞 OpenClaw angle
If you build self-hosted AI agents, use this as a checklist for your own model literacy: make sure your team can explain tokenization, attention, KV cache, and sampling settings before you ship anything. The README’s chapter structure is also a good template for internal onboarding docs: start with analogies, then worked examples, then annotated code. If you maintain local inference stacks, test both a tiny CPU model and a larger GPU config so your automation can fall back cleanly when hardware is limited.
The project covers the full pipeline for a decoder-only Transformer, the architecture used by systems in the GPT family. That includes tokenization with BPE, embeddings, positional encoding using RoPE, multi-head attention, transformer blocks, training loops, and inference features such as KV cache, temperature, top-k and top-p sampling, and beam search.
The author says the goal is not just to show that attention works, but to explain why. The material includes topics such as the variance argument behind 1/√d_k, how RoPE captures relative position through rotation, why pre-norm is used in deep networks, and where gradients flow during backpropagation.
The guide is organized into chapters that build on one another. The early sections cover setup and tokenization, while later chapters move into embeddings, positional encoding, attention, transformer blocks, the full GPT model, training, inference, and a glossary.
The README says the core model code is about 860 lines, with about 2,600 lines of explanation and diagrams. It also says the notebooks are designed to be run live, so readers can watch the model train on a very small dataset in minutes rather than weeks.
The project includes both a tiny configuration and a larger GPT-2-scale configuration. By default, the script uses a small model with 17 million parameters that can run on CPU in a few minutes, while the larger 151 million parameter version needs a GPU.
The author also says the guide uses publicly documented techniques found in models such as LLaMA 3, Mistral, and Qwen 2.5, while noting that GPT-4 and Claude architectures are proprietary and undisclosed. The repository is available on GitHub, and the README invites users to clone it, set up a Python virtual environment, install dependencies, and start with Chapter 0.
