graphdeco-inria / graphdeco-inria/gray

Release GRay pretrained models on Hugging Face

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Dominant language
Python
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Description

Hi @yohan-pg 🤗

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 to improve its discoverability. If you are one of the authors, you can submit it at https://huggingface.co/papers/submit.

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.

Would you like to host the pre-trained models you've released (currently hosted on your project server) on https://huggingface.co/models?
Since your project format uses .safetensors which is native and highly integrated on the Hugging Face Hub, hosting them on Hugging Face will give you more visibility and 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. You can directly upload the .safetensors model files via the Web UI or using the huggingface_hub Python library, and people can download them using hf_hub_download.

After uploaded, we can also link the models to the paper page (read here) so people can discover your model.

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

Kind regards,

Niels

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

No repository files or tests are identified in the issue. Start with the Hugging Face model-uploading guide and inspect the pretrained models currently hosted on the project server. Done means the relevant models are uploaded to Hugging Face with suitable model-card tags and linked to the paper page.

Written by the indexing model from the issue text.

Assessment

Tech stack
huggingface, python
Domain
machine-learning, release
Issue type
Feature
Difficulty
3/5
Estimated time
1-2 days
Activity status
Quiet
Clarity
Mostly clear
Newbie friendliness
45/100

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