Release SparseVideoNav artifacts (models, dataset) on Hugging Face

Hi @stdcat 🤗

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/2602.05827.
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 README that you plan to release the SparseVideoNav model checkpoints (distilled video generation and action head) and the 140h real-world VLN dataset later this year. It'd be great to make these available on the 🤗 hub when you release them, to improve their discoverability and 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, for your video generation or action models, you could leverage the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to any custom nn.Module. Alternatively, one can leverages the hf_hub_download 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.

Uploading dataset

Would be awesome to make the 140h dataset 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 first few rows of the data in the browser.

Let me know if you're interested or need any help regarding this when you get closer to your release dates!

Cheers,

Niels
ML Engineer @ HF 🤗

Beginne mit den geplanten Veröffentlichungsdetails im GitHub README sowie den verlinkten Anleitungen zum Hochladen von Modellen und Datensätzen auf Hugging Face. Als abgeschlossen gilt die Aufgabe, wenn die Modell-Checkpoints von SparseVideoNav und der 140-stündige VLN-Datensatz aus der Praxis auf Hugging Face mit separaten, auffindbaren Repositories und nutzbaren Anleitungen zum Laden veröffentlicht sind.