Release DASH checkpoints and code on Hugging Face #1
Description
Hi @ionutmodo 🤗
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.
I saw in your abstract that you plan to release the code at https://github.com/IST-DASLab/DASH. Would you also like to host the model checkpoints you've pre-trained (those achieving the impressive validation perplexity results) 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.
Since you mentioned DASH produces models with lower activation outliers that are easier to compress, it would be great for the community to have access to these checkpoints to explore those properties.
If you're down, leaving a guide here. If it's a custom PyTorch model, you can use the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to the model which lets you to upload the model and people to download and use models right away.
After uploaded, we can also link the models to the paper page so people can discover your work.
Let me know if you're interested/need any guidance :)
Kind regards,
Niels