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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 198 Bytes
f0c5b40 | 1 2 3 4 5 | Reparameterized Absorbing Discrete Diffusion (RADD) small model with lambda-dce loss trained for 400k iterations. Code: https://github.com/ML-GSAI/RADD. Paper: https://arxiv.org/abs/2406.03736. |