Hello,
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work through Papers with Code as your paper got featured: https://paperswithcode.co/paper/2602.23353.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models for instance), and you can also claim the paper as yours which will show up on your public profile at HF, as well as add GitHub and project page URLs.
I saw on your GitHub repository that you plan to release the full code soon. Once you are ready, would you like to host the pre-trained alignment layers/checkpoints you've trained on https://huggingface.co/models?
Hosting on Hugging Face will give your work more visibility and enable better discoverability. We can add metadata tags in the model cards so that people find the models easier, link them directly to the paper page, etc.
If you're down, leaving a guide 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, letting people download and use the models right away. Alternatively, you can directly upload your weights and users can load them via hf_hub_download.
After uploading, we can also link the models to the paper page (read here) so people can easily discover your model.
Let me know if you're interested or 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 Papers with Code as your paper got featured: https://paperswithcode.co/paper/2602.23353.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models for instance), and you can also claim the paper as yours which will show up on your public profile at HF, as well as add GitHub and project page URLs.
I saw on your GitHub repository that you plan to release the full code soon. Once you are ready, would you like to host the pre-trained alignment layers/checkpoints you've trained on https://huggingface.co/models?
Hosting on Hugging Face will give your work more visibility and enable better discoverability. We can add metadata tags in the model cards so that people find the models easier, link them directly to the paper page, etc.
If you're down, leaving a guide here. If it's a custom PyTorch model, you can use the PyTorchModelHubMixin class which adds
from_pretrainedandpush_to_hubto the model, letting people download and use the models right away. Alternatively, you can directly upload your weights and users can load them via hf_hub_download.After uploading, we can also link the models to the paper page (read here) so people can easily discover your model.
Let me know if you're interested or need any guidance!
Kind regards,
Niels