Release OmniParser V2 on Hugging Face

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](https://huggingface.co/docs/hub/models-uploading). For custom architectures, you can use the [PyTorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.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](https://huggingface.co/spaces). We can provide you with a [ZeroGPU grant](https://huggingface.co/docs/hub/en/spaces-gpus#community-gpu-grants), 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