Hi @zhibogogo 🤗
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 your latest paper on OmniParser V2 got featured: https://huggingface.co/papers/2502.16161.
The paper page lets people discuss your research and find related artifacts. You can also claim the paper as yours, which will show it on your public HF profile and allow you to link the GitHub and project URLs directly.
I saw in the paper that the code and models are being made available in the AdvancedLiterateMachinery repository. Would you like to host the pre-trained checkpoints for OmniParser V2 on https://huggingface.co/models?
Hosting on Hugging Face will provide significantly more visibility and enable better discoverability through our metadata tagging system (e.g., for image-text-to-text tasks). We can link the models directly to the paper page so that researchers can find and use them instantly.
If you're interested, I've left a guide here. For custom architectures, you can use the PyTorchModelHubMixin class to add from_pretrained and push_to_hub methods, making it very easy for the community to download and run your model.
We would also love to help you build a demo for OmniParser V2 on Spaces. We can provide you with a ZeroGPU grant, which gives you access to A100 GPUs for free to power the demo.
Let me know if you're interested or need any guidance!
Kind regards,
Niels
Hi @zhibogogo 🤗
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 your latest paper on OmniParser V2 got featured: https://huggingface.co/papers/2502.16161.
The paper page lets people discuss your research and find related artifacts. You can also claim the paper as yours, which will show it on your public HF profile and allow you to link the GitHub and project URLs directly.
I saw in the paper that the code and models are being made available in the
AdvancedLiterateMachineryrepository. Would you like to host the pre-trained checkpoints for OmniParser V2 on https://huggingface.co/models?Hosting on Hugging Face will provide significantly more visibility and enable better discoverability through our metadata tagging system (e.g., for
image-text-to-texttasks). We can link the models directly to the paper page so that researchers can find and use them instantly.If you're interested, I've left a guide here. For custom architectures, you can use the PyTorchModelHubMixin class to add
from_pretrainedandpush_to_hubmethods, making it very easy for the community to download and run your model.We would also love to help you build a demo for OmniParser V2 on Spaces. We can provide you with a ZeroGPU grant, which gives you access to A100 GPUs for free to power the demo.
Let me know if you're interested or need any guidance!
Kind regards,
Niels