Release MedSAE on Hugging Face
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 35/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Quiet
- Tech stack
- huggingface, python
- Domain
- machine-learning, release
Research direction
Start with the MedSAE checkpoints referenced in the issue and review the Hugging Face model-uploading guide. Determine which pretrained artifacts should be published and whether the repository uses a custom PyTorch model; done means the selected checkpoints are hosted on Hugging Face and linked to the paper page.
Written by the indexing model from the issue text.
Description
Hi @riccardorenzulli 🤗
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 MedSAE checkpoints you've pre-trained on https://huggingface.co/models?
Hosting on Hugging Face will give you more visibility/enable better discoverability within the mechanistic interpretability community. 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. 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.
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.
After uploaded, we can also link the models to the paper page (read here) so people can discover your model.
You can also build a demo for your model on Spaces, we can provide you a ZeroGPU grant, which gives you A100 GPUs for free.
Let me know if you're interested/need any guidance :)
Kind regards,
Niels
- Dominant language
- Python
- Stars
- 1
- Forks
- 0
- PR merge metrics
- No merged PRs in 30d
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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