Hi @seashell11 🤗
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/2606.12384.
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.
I noticed in your GitHub repository (https://github.com/AMAP-ML/APPO) that you have a script to convert VERL checkpoints to Hugging Face format (convert_checkpoint_from_verl_to_hf_qwen3.sh). This is great and indicates that you have trained models (e.g., APPO-Qwen3-7B-Reasoning, APPO-Qwen3-8B-Deepsearch, APPO-Qwen3-14B-Deepsearch, and APPO-Llama) with your APPO method!
Would you like to host these models you've pre-trained on https://huggingface.co/models?
Hosting on Hugging Face will give you more visibility/enable better discoverability. We can add tags in the model cards so that people find the models easier, link it to the paper page, etc.
If you're down, leaving a guide here. In this case, we could leverage the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to any custom nn.Module (like your VERL checkpoints after conversion). Alternatively, one can leverage the hf_hub_download one-liner to download a checkpoint from the hub.
We encourage researchers to push each model checkpoint to a separate model repository, so that things like download stats also work. We can then also link the checkpoints to the paper page (read here) so people can discover your model.
You can also build a demo for your model on Spaces, we can provide you a ZeroGPU grant, which gives you A100 GPUs for free.
Let me know if you're interested/need any guidance :)
Kind regards,
Niels
Hi @seashell11 🤗
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/2606.12384.
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.
I noticed in your GitHub repository (https://github.com/AMAP-ML/APPO) that you have a script to convert VERL checkpoints to Hugging Face format (
convert_checkpoint_from_verl_to_hf_qwen3.sh). This is great and indicates that you have trained models (e.g., APPO-Qwen3-7B-Reasoning, APPO-Qwen3-8B-Deepsearch, APPO-Qwen3-14B-Deepsearch, and APPO-Llama) with your APPO method!Would you like to host these models you've pre-trained on https://huggingface.co/models?
Hosting on Hugging Face will give you more visibility/enable better discoverability. We can add tags in the model cards so that people find the models easier, link it to the paper page, etc.
If you're down, leaving a guide here. In this case, we could leverage the PyTorchModelHubMixin class which adds
from_pretrainedandpush_to_hubto any customnn.Module(like your VERL checkpoints after conversion). Alternatively, one can leverage the hf_hub_download one-liner to download a checkpoint from the hub.We encourage researchers to push each model checkpoint to a separate model repository, so that things like download stats also work. We can then also link the checkpoints to the paper page (read here) so people can discover your model.
You can also build a demo for your model on Spaces, we can provide you a ZeroGPU grant, which gives you A100 GPUs for free.
Let me know if you're interested/need any guidance :)
Kind regards,
Niels