Release artifacts (models, dataset) on Hugging Face
Nobody has claimed this yet.
Assessment
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Newbie friendliness
- 35/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- huggingface, python
- Domain
- machine-learning, release
Research direction
No files or tests are named. Review the repository's existing model checkpoints, then inspect the GPTQ and BitBLAS model entries and their Hugging Face paper links. Done means the relevant checkpoints are available on the Hugging Face Hub and linked to the paper.
Written by the indexing model from the issue text.
Description
Hi @ChenMnZ 🤗
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/2407.11062.
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.
It'd be great to make all checkpoints available on the 🤗 hub and linked to the paper, to improve their discoverability/visibility.
I've noticed that the GPTQ and BitBLAS versions of the models have not yet been linked to the paper, I will add those now.
Cheers,
Niels
ML Engineer @ HF 🤗
- Dominant language
- Python
- Stars
- 351
- Forks
- 38
- PR merge metrics
- No merged PRs in 30d
Contributor guide
No contributing guide indexed for this repository
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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