Hi @AuroraZengfh 🤗
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/2605.25437.
The paper page lets people find artifacts about it; I see you have already uploaded the MARS dataset to the Hub, which is great! You can also claim the paper as yours which will show up on your public profile at HF, and add GitHub and project page URLs to the paper page.
I noticed in your GitHub repository's TODO list that you plan to release the pre-trained model checkpoints. Would you like to host those models on https://huggingface.co/models as well?
Hosting on Hugging Face will give your work more visibility and enable better discoverability through metadata tags. If you're interested, you can use the PyTorchModelHubMixin class to add from_pretrained and push_to_hub methods to your custom models, or simply upload them through the UI.
After they are uploaded, we can also link the models to the paper page (read here) so researchers can easily find all related artifacts (dataset and models) in one place.
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 or need any guidance!
Kind regards,
Niels
Hi @AuroraZengfh 🤗
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/2605.25437.
The paper page lets people find artifacts about it; I see you have already uploaded the MARS dataset to the Hub, which is great! You can also claim the paper as yours which will show up on your public profile at HF, and add GitHub and project page URLs to the paper page.
I noticed in your GitHub repository's TODO list that you plan to release the pre-trained model checkpoints. Would you like to host those models on https://huggingface.co/models as well?
Hosting on Hugging Face will give your work more visibility and enable better discoverability through metadata tags. If you're interested, you can use the PyTorchModelHubMixin class to add
from_pretrainedandpush_to_hubmethods to your custom models, or simply upload them through the UI.After they are uploaded, we can also link the models to the paper page (read here) so researchers can easily find all related artifacts (dataset and models) in one place.
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 or need any guidance!
Kind regards,
Niels