Release finetuned tokenizer deltas on Hugging Face

Hi @ilabiad 🤗

Niels here from the open-source team at Hugging Face. I discovered your work through Hugging Face's daily papers as yours got featured: https://huggingface.co/papers/2506.16349.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models, datasets or demo for instance), you can also claim
the paper as yours which will show up on your public profile at HF, add Github and project page URLs.

It'd be great to make the checkpoints and dataset available on the 🤗 hub, to improve their discoverability/visibility.
We can add tags so that people find them when filtering https://huggingface.co/models and https://huggingface.co/datasets.

## Uploading models

See here for a guide: https://huggingface.co/docs/hub/models-uploading.

In this case, we could leverage the [PyTorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) class which adds `from_pretrained` and `push_to_hub` to any custom `nn.Module`. Alternatively, one can leverages the [hf_hub_download](https://huggingface.co/docs/huggingface_hub/en/guides/download#download-a-single-file) one-liner to download a checkpoint from the hub.

We encourage researchers to push each model checkpoint to a separate model repository, so that things like download stats also work. We can then also link the checkpoints to the paper page.

Let me know if you're interested/need any help regarding this!

Cheers,

Niels
ML Engineer @ HF 🤗

Start by locating the finetuned tokenizer deltas, checkpoints, and dataset in the repository, then read Hugging Face's model-uploading guide and the PyTorchModelHubMixin documentation. Done means the requested artifacts are published as discoverable Hugging Face model and dataset repositories and linked to the paper page.

Written by the indexing model from the issue text.