How to build a LLM from scratch (and what it teaches you)

TL;DR Building a minimal language model from scratch takes fewer than 300 lines of Python. The process reveals exactly how tokenization, attention, and inference work, which makes you a far better API consumer when you're integrating production LLMs into your applications. Try Apidog today Introduction Most developers treat language models as black boxes. You send text in, tokens come out, and somewhere in between, magic happens. That mental model works fine until you need to debug a broken API integration, tune sampling parameters, or figure out why your model keeps hallucinating structured data. GuppyLM, a project that recently hit the HackerNews front page with 842 points, makes the internals visible. It's a 8.7M parameter transformer written from scratch in Python. It trains in under an hour on a consumer GPU. The code fits in a single file. The goal isn't to compete with GPT-4; it's to demystify what LLMs actually do. This article walks through how to build a tiny LLM, what each c