Instructions to use ljsabc/Fujisaki-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ljsabc/Fujisaki-int4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ljsabc/Fujisaki-int4", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ljsabc/Fujisaki-int4", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6589d33adf3d46bd6e1da9eed69950ef7bcb804fcc0f3b9161be52c7437ea081
- Size of remote file:
- 5.3 GB
- SHA256:
- 340b7dc0fe1795d5c42a3e07ca765afb6652660473c8b74fa681223949fc80a5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.