Instructions to use kamizane/FineTuningJsonscheme3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kamizane/FineTuningJsonscheme3B with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kamizane/FineTuningJsonscheme3B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8604a480456318a7b31370a27b4e50b161f2bebaeba2b2853c93615671814dfd
- Size of remote file:
- 99.2 MB
- SHA256:
- 8acb258e37671e0d10d6bc38f683eb9a300feaa8ca2db10d359fe66082fa4c8d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.