Instructions to use jinzhuoran/OmniRewardModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinzhuoran/OmniRewardModel with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jinzhuoran/OmniRewardModel", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag, library name, paper, and code links to model card
#2
by nielsr HF Staff - opened
This PR enhances the model card for Omni-Reward by:
- Adding
library_name: transformersto correctly enable the "how to use" widget, as the model's file structure (e.g.,config.json,tokenizer_config.json) indicates compatibility with the Hugging Face Transformers library. - Setting
pipeline_tag: any-to-anyto accurately reflect its omni-modal capabilities (handling text, image, video, audio, and 3D data) and improve discoverability on the Hub. - Including a direct link to the paper (Omni-Reward: Towards Generalist Omni-Modal Reward Modeling with Free-Form Preferences) and the official GitHub repository (
https://github.com/HongbangYuan/OmniReward) in the introductory link section. - Correcting a minor
</a></a>typo in the existing benchmark link.
These updates improve the model's discoverability and provide users with comprehensive information and usage guidance.
jinzhuoran changed pull request status to merged