# Putting my JAX-trained models on the Hugging Face Hub > Source: > Published: 2026-09-03 17:25:17.019712+00:00 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.