Putting my JAX-trained models on the Hugging Face Hub

Developer gpjt has uploaded PyTorch-compatible versions of all his JAX-trained GPT-2 models to the Hugging Face Hub, including models from his blog series on writing an LLM from scratch and Chinchilla scaling experiments. The uploads enable use with Transformers' AutoModelForCausalLM, which has been PyTorch-only since version 5.

I hadn't uploaded the models that I trained using JAX to the Hugging Face Hub because Transformers has been PyTorch-only since version 5 https://huggingface.co/blog/transformers-v5 though they say they're working to add interoperability with JAX in the future , so it would have been tough to get them working natively with AutoModelForCausalLM and the like. But then it dawned on me that I'd already written a conversion script https://github.com/gpjt/jax-gpt2-from-scratch/blob/main/convert model to pytorch.py that could take my JAX safetensors files and convert them into ones compatible with my PyTorch code. It's actually those converted models that I use for my evals So, I've now uploaded PyTorch-compatible versions of all of my JAX-trained models: " Writing an LLM from scratch, part 34b -- from bigrams to GPT-2, one component at a time in JAX /2026/07/llm-from-scratch-34b-building-and-training-gpt-2-small-in-jax " gpjt/jax-no-mha-bias-no-dropout gpjt/jax-no-mha-bias-with-dropout gpjt/jax-with-mha-bias-no-dropout " Why do OpenAI's GPT-2 weights beat mine? Part three: testing overtraining /2026/07/why-do-openai-gpt2-weights-beat-mine-3-overtraining " gpjt/jax-with-mha-bias-no-dropout-extended gpjt/jax-with-mha-bias-no-dropout-2-epoch " A quick ish Chinchilla check /2026/08/chinchilla-check " gpjt/jax-with-mha-bias-larger-chinchilla-1 slightly-larger model. gpjt/jax-with-mha-bias-larger-chinchilla-2 slightly-smaller model.I've also added links to the posts in question.