Instructions to use Coldbrew9/Edentns-DataVortexS-trainWithCombi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Coldbrew9/Edentns-DataVortexS-trainWithCombi with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Coldbrew9/Edentns-DataVortexS-trainWithCombi", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d76ae306b6eb912d542512bb5c8a1abf6674783fc453195a4d98a6aaaa038545
- Size of remote file:
- 69.3 MB
- SHA256:
- c4118a75df0475ea253401b9dfa61e77ece5dffcd6a14a9f699c85f7bf125ded
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.