Hi @P-YI 🤗
Niels here from 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/2509.25843.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models, datasets or demo 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.
Your work on ASGuard offers a very insightful mechanistic approach to mitigate targeted jailbreaking attacks in LLMs. I noticed in your GitHub README (https://github.com/dmis-lab/ASGuard) that the full implementation for "circuit construction" and "evaluation" is still pending with "Please wait for an update" messages.
It'd be great to make the ASGuard-enhanced versions of the LLMs you worked on (Llama-3.1-8B-Instruct, Qwen2.5-7B-Instruct, gemma-2-9b-it, OLMo-2-1124-7B-Instruct), as well as any associated "Refusal Dataset" used for preventative fine-tuning, available on the 🤗 hub once they are ready. This would significantly improve their discoverability and visibility.
We can add tags so that people find them when filtering https://huggingface.co/models and https://huggingface.co/datasets.
Uploading models
See here for a guide: https://huggingface.co/docs/hub/models-uploading.
In this case, we could leverage the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to any custom nn.Module. Alternatively, one can leverages 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.
Uploading dataset
Would be awesome to make the dataset available on 🤗 , so that people can do:
from datasets import load_dataset
dataset = load_dataset("your-hf-org-or-username/your-dataset")See here for a guide: https://huggingface.co/docs/datasets/loading.
Besides that, there's the dataset viewer which allows people to quickly explore the first few rows of the data in the browser.
Let me know if you're interested/need any help regarding this once the full artifacts are ready!
Cheers,
Niels
ML Engineer @ HF 🤗
Hi @P-YI 🤗
Niels here from 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/2509.25843.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models, datasets or demo 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.
Your work on ASGuard offers a very insightful mechanistic approach to mitigate targeted jailbreaking attacks in LLMs. I noticed in your GitHub README (https://github.com/dmis-lab/ASGuard) that the full implementation for "circuit construction" and "evaluation" is still pending with "Please wait for an update" messages.
It'd be great to make the ASGuard-enhanced versions of the LLMs you worked on (Llama-3.1-8B-Instruct, Qwen2.5-7B-Instruct, gemma-2-9b-it, OLMo-2-1124-7B-Instruct), as well as any associated "Refusal Dataset" used for preventative fine-tuning, available on the 🤗 hub once they are ready. This would significantly improve their discoverability and visibility.
We can add tags so that people find them when filtering https://huggingface.co/models and https://huggingface.co/datasets.
Uploading models
See here for a guide: https://huggingface.co/docs/hub/models-uploading.
In this case, we could leverage the PyTorchModelHubMixin class which adds
from_pretrainedandpush_to_hubto any customnn.Module. Alternatively, one can leverages 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.
Uploading dataset
Would be awesome to make the dataset available on 🤗 , so that people can do:
See here for a guide: https://huggingface.co/docs/datasets/loading.
Besides that, there's the dataset viewer which allows people to quickly explore the first few rows of the data in the browser.
Let me know if you're interested/need any help regarding this once the full artifacts are ready!
Cheers,
Niels
ML Engineer @ HF 🤗