Hi @numbmelon 🤗
Niels here from the open-source team at Hugging Face. I discovered your work on Arxiv and your GitHub repository for "OS-Oracle: A Comprehensive Framework for Cross-Platform GUI Critic Models" (https://github.com/numbmelon/OS-Oracle). It's great to see that the OS-Critic Bench dataset is already available on the Hugging Face Hub!
I also noticed from your README's TODO list that you plan to "Release model checkpoints" for OS-Oracle-7B and "Release training datasets" (referring to the 310k critic samples mentioned in the paper). It would be fantastic to have these additional artifacts available on the 🤗 Hub as well, to improve their discoverability and visibility. We can add tags so that people can easily 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 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. For your OS-Oracle-7B model, a pipeline tag like image-text-to-text would be highly relevant for discoverability.
Uploading dataset
Would be awesome to make the "310k critic samples" training 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. For your training dataset, a task category like image-text-to-text would be suitable.
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!
Cheers,
Niels
ML Engineer @ HF 🤗
Hi @numbmelon 🤗
Niels here from the open-source team at Hugging Face. I discovered your work on Arxiv and your GitHub repository for "OS-Oracle: A Comprehensive Framework for Cross-Platform GUI Critic Models" (https://github.com/numbmelon/OS-Oracle). It's great to see that the OS-Critic Bench dataset is already available on the Hugging Face Hub!
I also noticed from your README's TODO list that you plan to "Release model checkpoints" for OS-Oracle-7B and "Release training datasets" (referring to the 310k critic samples mentioned in the paper). It would be fantastic to have these additional artifacts available on the 🤗 Hub as well, to improve their discoverability and visibility. We can add tags so that people can easily 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 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. For your OS-Oracle-7B model, a pipeline tag like
image-text-to-textwould be highly relevant for discoverability.Uploading dataset
Would be awesome to make the "310k critic samples" training dataset available on 🤗 , so that people can do:
See here for a guide: https://huggingface.co/docs/datasets/loading. For your training dataset, a task category like
image-text-to-textwould be suitable.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!
Cheers,
Niels
ML Engineer @ HF 🤗