Instructions to use akkikiki/LLaDA-8B-Instruct-judge-fs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akkikiki/LLaDA-8B-Instruct-judge-fs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="akkikiki/LLaDA-8B-Instruct-judge-fs", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("akkikiki/LLaDA-8B-Instruct-judge-fs", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fb2c128e5fb32e89721a2156a8200ff9126e7a86398629e3103b881dd26eedff
- Size of remote file:
- 1.04 GB
- SHA256:
- 62b32ee37d0ac805e7232188acac90af2504ba6f067b5ef058bb16588fe05e58
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.