Instructions to use JingyangOu/radd-lambda-dce with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JingyangOu/radd-lambda-dce with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JingyangOu/radd-lambda-dce", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag and improve model card
#1
by nielsr HF Staff - opened
Hi! I'm Niels, part of the community science team at Hugging Face.
I've opened this PR to improve the model card for RADD:
- Added
pipeline_tag: text-generationto the metadata to improve discoverability on the Hub. - Added links to the official paper and GitHub repository.
- Included a sample usage snippet for loading the model, as documented in your repository.
- Added the BibTeX citation for researchers.