Release Gwhere on Hugging Face

Hi @alibaba 🤗

I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work on Arxiv and was wondering whether you would like to submit it to [hf.co/papers](https://hf.co/papers) to improve its discoverability. If you are one of the authors, you can submit it at https://huggingface.co/papers/2607.26073

The paper page lets people discuss about your paper and lets them find artifacts about it (your models 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.

I noticed in the Github repository that the LLM training code is "coming soon". Once the Gwhere checkpoints are ready, would you like to host them on https://huggingface.co/models?
Hosting on Hugging Face will give you more visibility/enable better discoverability. We can add tags in the model cards so that people find the models easier, link it to the paper page, etc.

If you're down, leaving a guide [here](https://huggingface.co/docs/hub/models-uploading). If it's a custom PyTorch model, you can use the [PyTorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) class which adds `from_pretrained` and `push_to_hub` to the model which lets you upload the model and people to download and use models right away. If you do not want this and directly want to upload model through UI or however you want, people can also use [hf_hub_download](https://huggingface.co/docs/huggingface_hub/en/guides/download#download-a-single-file).

After uploaded, we can also link the models to the paper page (read [here](https://huggingface.co/docs/hub/en/model-cards#linking-a-paper)) so people can discover your model.

You can also build a demo for your model on [Spaces](https://huggingface.co/spaces). [ZeroGPU](https://huggingface.co/docs/hub/en/spaces-gpus#zerogpu) gives on-demand GPU-backed compute for demo Spaces. For a limited time, users can create up to two ZeroGPU Spaces in their personal namespace for free.

Let me know if you're interested/need any guidance :)

Kind regards,

Niels

Start by checking whether the Gwhere checkpoints are ready and review the repository's planned LLM training work, which the issue says is coming soon. Read Hugging Face's model-uploading and model-card guidance, then determine the upload and paper-linking steps. Done would mean the intended checkpoints are hosted with discoverability metadata, if the maintainers agree to proceed.

Written by the indexing model from the issue text.