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Release benchmark audit artifacts (probe model, diagnostics) on Hugging Face #1

@NielsRogge

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@NielsRogge

Hi @Tianchong-Jiang 🤗

Niels here from 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, 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.

I saw in your GitHub repository that you are currently preparing the code and artifacts (like the 0.09B probe model and diagnostics) for release. It'd be great to make the checkpoints and any associated datasets available on the 🤗 hub, to improve their discoverability and visibility within the robotics community.

Uploading models

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

In this case, you could leverage the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to any custom nn.Module. Alternatively, one can leverage the hf_hub_download one-liner to download a checkpoint from the hub.

Uploading datasets

It would also be awesome to make the diagnostic data or result summaries available on 🤗, so that people can do:

from datasets import load_dataset

dataset = load_dataset("your-hf-org-or-username/your-dataset")

See here for a guide: https://huggingface.co/docs/datasets/loading.

Besides that, there's the dataset viewer which allows people to quickly explore the data in the browser.

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

Cheers,

Niels
ML Engineer @ HF 🤗

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