Hi @aronasefaw 馃
Niels here from 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, datasets or demo for instance), 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.
It'd be great to make the pre-trained WeightCLIP checkpoints and the trained model zoos available on the 馃 hub, to 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 (WeightCLIP Checkpoints)
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 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 download stats and model cards work beautifully. We can then also link the checkpoints directly to the paper page.
Uploading datasets (Model Zoos)
Since weight space learning relies heavily on populations of neural networks (model zoos), it would be awesome to make these zoos available on 馃 as datasets, so that people can easily stream or download them:
from datasets import load_dataset
dataset = load_dataset("your-hf-org-or-username/your-model-zoo")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 or need any help setting this up!
Cheers,
Niels
ML Engineer @ HF 馃
Hi @aronasefaw 馃
Niels here from 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, datasets or demo for instance), 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.
It'd be great to make the pre-trained WeightCLIP checkpoints and the trained model zoos available on the 馃 hub, to 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 (WeightCLIP Checkpoints)
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 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 download stats and model cards work beautifully. We can then also link the checkpoints directly to the paper page.
Uploading datasets (Model Zoos)
Since weight space learning relies heavily on populations of neural networks (model zoos), it would be awesome to make these zoos available on 馃 as datasets, so that people can easily stream or download them:
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 or need any help setting this up!
Cheers,
Niels
ML Engineer @ HF 馃