Hi @THeWakeSystems 🤗
Niels here from the open-source team at Hugging Face. I discovered your work on Arxiv ("RiverONE: Generating Knowledge-Intensive VLM by Simulated Quantum Machines") and was wondering whether you would like to submit it to hf.co/papers to improve its discoverability. If you are one of the authors, you can submit it at https://huggingface.co/papers/submit.
The paper page lets people discuss about your paper and find artifacts linked to it (like your models). 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.
Are you planning to release the pre-trained checkpoints for RiverONE (such as RiverONE-QC-4B-v1)?
If so, would you like to host the model weights on https://huggingface.co/models?
Hosting on Hugging Face will give you more visibility and enable better discoverability. We can add tags in the model cards so that people can find the models easily, link them directly to the paper page, etc.
If you're down, leaving a guide here. Since RiverONE is built with an InternVL-based language backbone, it could easily be uploaded. Alternatively, if you want to use a custom PyTorch class, you can leverage the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to the model. Otherwise, users can also use hf_hub_download to easily load the weights.
After uploaded, we can also link the models to the paper page (read here) so people can discover your model.
You can also build a demo for your model on Spaces, and 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
ML Engineer @ HF 🤗
Hi @THeWakeSystems 🤗
Niels here from the open-source team at Hugging Face. I discovered your work on Arxiv ("RiverONE: Generating Knowledge-Intensive VLM by Simulated Quantum Machines") and was wondering whether you would like to submit it to hf.co/papers to improve its discoverability. If you are one of the authors, you can submit it at https://huggingface.co/papers/submit.
The paper page lets people discuss about your paper and find artifacts linked to it (like your models). 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.
Are you planning to release the pre-trained checkpoints for RiverONE (such as
RiverONE-QC-4B-v1)?If so, would you like to host the model weights on https://huggingface.co/models?
Hosting on Hugging Face will give you more visibility and enable better discoverability. We can add tags in the model cards so that people can find the models easily, link them directly to the paper page, etc.
If you're down, leaving a guide here. Since RiverONE is built with an InternVL-based language backbone, it could easily be uploaded. Alternatively, if you want to use a custom PyTorch class, you can leverage the PyTorchModelHubMixin class which adds
from_pretrainedandpush_to_hubto the model. Otherwise, users can also use hf_hub_download to easily load the weights.After uploaded, we can also link the models to the paper page (read here) so people can discover your model.
You can also build a demo for your model on Spaces, and 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
ML Engineer @ HF 🤗