Hi @HCPLab-SYSU 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 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's fantastic to see that you've already released the model weights on Hugging Face! To further enhance their usability and discoverability, would you be interested in converting these into dedicated model repositories? Currently, they're hosted as individual blob files within your repository, which makes them less discoverable and doesn't allow users to easily leverage features like download statistics, versioning, and direct integration via transformers.AutoModel.
By creating separate model repositories for each checkpoint, users could easily load and utilize your models with just a few lines of code:
from transformers import AutoModel
model = AutoModel.from_pretrained("HCPLab-SYSU/darvit-tiny") # ExampleWe 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.
If you're interested, here's a guide to get you started: https://huggingface.co/docs/hub/models-uploading. You can leverage the PyTorchModelHubMixin class, which simplifies the process with from_pretrained and push_to_hub methods. Alternatively, you can use hf_hub_download for a one-line checkpoint download.
After uploading, we can 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 or need any assistance with this!
Kind regards,
Niels
ML Engineer @ HF 馃
Hi @HCPLab-SYSU 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 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's fantastic to see that you've already released the model weights on Hugging Face! To further enhance their usability and discoverability, would you be interested in converting these into dedicated model repositories? Currently, they're hosted as individual blob files within your repository, which makes them less discoverable and doesn't allow users to easily leverage features like download statistics, versioning, and direct integration via
transformers.AutoModel.By creating separate model repositories for each checkpoint, users could easily load and utilize your models with just a few lines of code:
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.
If you're interested, here's a guide to get you started: https://huggingface.co/docs/hub/models-uploading. You can leverage the PyTorchModelHubMixin class, which simplifies the process with
from_pretrainedandpush_to_hubmethods. Alternatively, you can usehf_hub_downloadfor a one-line checkpoint download.After uploading, we can 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 or need any assistance with this!
Kind regards,
Niels
ML Engineer @ HF 馃