Hi @allencbzhang 🤗
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/2605.31096.
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 models you've pre-trained (iVGR-Qwen2.5-VL-7B, iVGR-Qwen3-VL-8B, and iVGR-Qwen3-VL-32B) on https://huggingface.co/models?
I see you've already included a Hugging Face collection link in your README, which is a great start! 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 them to the paper page, etc.
If you're down, leaving a guide here. Since these are based on Qwen-VL backbones, they fit the image-text-to-text pipeline tag. If you use a custom PyTorch implementation, you can use the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to the model. Alternatively, people can use hf_hub_download to download files directly.
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
Hi @allencbzhang 🤗
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/2605.31096.
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 models you've pre-trained (iVGR-Qwen2.5-VL-7B, iVGR-Qwen3-VL-8B, and iVGR-Qwen3-VL-32B) on https://huggingface.co/models?
I see you've already included a Hugging Face collection link in your README, which is a great start! 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 them to the paper page, etc.
If you're down, leaving a guide here. Since these are based on Qwen-VL backbones, they fit the
image-text-to-textpipeline tag. If you use a custom PyTorch implementation, you can use the PyTorchModelHubMixin class which addsfrom_pretrainedandpush_to_hubto the model. Alternatively, people can use hf_hub_download to download files directly.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