Instructions to use hanlincs/Bifrost-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hanlincs/Bifrost-1 with Transformers:
# Load model directly from transformers import MultiModalityCausalLM model = MultiModalityCausalLM.from_pretrained("hanlincs/Bifrost-1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card for Bifrost-1
#2
by nielsr HF Staff - opened
This PR significantly enhances the model card for Bifrost-1 by adding:
- Essential metadata such as
pipeline_tag: text-to-image,library_name: transformers, andlicense: apache-2.0, improving discoverability and categorization on the Hub. - A direct link to the Hugging Face paper page.
- A link to the official GitHub repository for source code and more information.
- Detailed usage instructions for environment setup and inference, directly from the project's GitHub README.
- The BibTeX citation and acknowledgements section.
- An updated overview of the model's features and an additional architecture image.
These improvements provide users with a comprehensive overview and quick start guide for Bifrost-1.
hanlincs changed pull request status to merged