Hi @aHapBean 🤗
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work on Arxiv 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 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 saw in your GitHub README that you are planning to release the NITP implementation soon. Would you also be open to hosting the pre-trained model checkpoints (from the 0.5B to the 9B MoE models) on https://huggingface.co/models? Hosting on Hugging Face will give your work more visibility and enable better discoverability. We can add metadata tags to the model cards so that people find the models easier, link them directly to the paper page, etc.
If you're down, I'm leaving a guide here. If it's a custom PyTorch model, you can use the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to the model which lets you upload the model and allows people to download and use models right away.
After uploaded, we can also link the models to the paper page (read here) so people can discover your work.
Let me know if you're interested/need any guidance :)
Kind regards,
Niels
Hi @aHapBean 🤗
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work on Arxiv 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 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 saw in your GitHub README that you are planning to release the NITP implementation soon. Would you also be open to hosting the pre-trained model checkpoints (from the 0.5B to the 9B MoE models) on https://huggingface.co/models? Hosting on Hugging Face will give your work more visibility and enable better discoverability. We can add metadata tags to the model cards so that people find the models easier, link them directly to the paper page, etc.
If you're down, I'm leaving a guide here. If it's a custom PyTorch model, you can use the PyTorchModelHubMixin class which adds
from_pretrainedandpush_to_hubto the model which lets you upload the model and allows people to download and use models right away.After uploaded, we can also link the models to the paper page (read here) so people can discover your work.
Let me know if you're interested/need any guidance :)
Kind regards,
Niels