Instructions to use diffusion-reasoning/LLaDA-8B-Instruct-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use diffusion-reasoning/LLaDA-8B-Instruct-SFT with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("diffusion-reasoning/LLaDA-8B-Instruct-SFT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff31f3bb5811ba5c22c8383a3de5af220adbeced84f3757beb130eb48638ca4f
- Size of remote file:
- 403 MB
- SHA256:
- a28bdc951aefb4222bfd99242c4c2638ac928fde0d30704fa9eb91ff22e768e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.