Hi @yangqy1 馃
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 or code 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 that you are planning to release the implementation of TAPPA at https://github.com/MIRALab-USTC/LLM-TAPPA. Your results on LLM structural pruning are particularly interesting. Would you like to host the code and any pruned model checkpoints you've produced on https://huggingface.co/models? Hosting on Hugging Face will give your work more visibility and enable better discoverability. We can add tags in the model cards so that people find the artifacts easier, link them to the paper page, etc.
If you're down, leaving a guide here. If you release custom model weights, you can use the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to the model, allowing people to download and use them right away.
Let me know if you're interested/need any guidance :)
Kind regards,
Niels
ML Engineer @ HF 馃
Hi @yangqy1 馃
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 or code 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 that you are planning to release the implementation of TAPPA at https://github.com/MIRALab-USTC/LLM-TAPPA. Your results on LLM structural pruning are particularly interesting. Would you like to host the code and any pruned model checkpoints you've produced on https://huggingface.co/models? Hosting on Hugging Face will give your work more visibility and enable better discoverability. We can add tags in the model cards so that people find the artifacts easier, link them to the paper page, etc.
If you're down, leaving a guide here. If you release custom model weights, you can use the PyTorchModelHubMixin class which adds
from_pretrainedandpush_to_hubto the model, allowing people to download and use them right away.Let me know if you're interested/need any guidance :)
Kind regards,
Niels
ML Engineer @ HF 馃