Instructions to use das152/gemma-gold with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use das152/gemma-gold with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("das152/gemma-gold", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 58610ae638246a0472954d06e2f72e6765c428871abadff88fb09c675cd9bed0
- Size of remote file:
- 33.4 MB
- SHA256:
- a74aefb1dc1340a25f29ab8370384b9ed24b2d921d7749ece7bbcfcfdf00d497
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.