Industry-lab technical reports have thinned out, but when weights ship on the Hugging Face Hub and the model is supported in the transformers library, the config file plus the reference implementation supply the architecture details the paper skipped.
The method only reaches open weightsA model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.Full definition → models. For ChatGPT, Claude, or Gemini there is no config or reference code to read, so architectural claims about them stay unverifiable from the outside.
Keep the process manual on purpose. Parts of the config-to-diagram walk could be scripted, but doing a handful of architectures by hand is what actually builds the understanding you were after.
Terms in this piece · Glossary
open weights — A model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
Why it matters
If you want to genuinely understand how a new open-weight model works, this shows you how to decode Hugging Face config files and reference implementations directly — trusting working code over increasingly vague technical papers.
Key quotes
“But if the goal is to learn how these architectures work, then doing a few of these by hand is, in my opinion, still one of the best exercises.”
