Hello,
I'm Niels and work as part of 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/2605.28992.
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 FRAPPE-Image models you've pre-trained 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 have an existing library (compressors), you could even leverage the hf_hub_download one-liner to download the checkpoints directly from the hub into your library. Alternatively, if it's a custom PyTorch model, you can use the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to the model.
After uploaded, we can also link the models to the paper page (read here) so people can discover your work.
You can also build a demo for your model on Spaces, we can provide you a ZeroGPU grant, which gives you free GPU-backed compute for eligible demo Spaces.
Let me know if you're interested/need any guidance :)
Kind regards,
Niels
Hello,
I'm Niels and work as part of 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/2605.28992.
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 FRAPPE-Image models you've pre-trained 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 have an existing library (
compressors), you could even leverage the hf_hub_download one-liner to download the checkpoints directly from the hub into your library. Alternatively, if it's a custom PyTorch model, you can use the PyTorchModelHubMixin class which addsfrom_pretrainedandpush_to_hubto the model.After uploaded, we can also link the models to the paper page (read here) so people can discover your work.
You can also build a demo for your model on Spaces, we can provide you a ZeroGPU grant, which gives you free GPU-backed compute for eligible demo Spaces.
Let me know if you're interested/need any guidance :)
Kind regards,
Niels