Hi @weigertlab 馃
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work through Hugging Face Daily Papers: https://huggingface.co/papers/2608.16810
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 model you've pre-trained on https://huggingface.co/models?
Hosting on Hugging Face will give you more visibility and 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.
If you're down, a guide is available 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 upload the model and people to download and use it right away. Alternatively, you can directly upload model files through the UI, and people can use hf_hub_download.
After uploading, 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. ZeroGPU gives on-demand GPU-backed compute for demo Spaces. For a limited time, users can create up to two ZeroGPU Spaces in their personal namespace for free.
Let me know if you're interested/need any guidance :)
Kind regards,
Niels
Hi @weigertlab 馃
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work through Hugging Face Daily Papers: https://huggingface.co/papers/2608.16810
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 model you've pre-trained on https://huggingface.co/models?
Hosting on Hugging Face will give you more visibility and 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.
If you're down, a guide is available here. If it's a custom PyTorch model, you can use the PyTorchModelHubMixin class which adds
from_pretrainedandpush_to_hubto the model which lets you upload the model and people to download and use it right away. Alternatively, you can directly upload model files through the UI, and people can use hf_hub_download.After uploading, 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. ZeroGPU gives on-demand GPU-backed compute for demo Spaces. For a limited time, users can create up to two ZeroGPU Spaces in their personal namespace for free.
Let me know if you're interested/need any guidance :)
Kind regards,
Niels