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:
- 9e625d64a867df933c0348588a1331e65a618c9634251e4c98135d2e09677af3
- Size of remote file:
- 99.2 MB
- SHA256:
- aad9930680fa66c10ef26cf4724211658a79571575dd3f1962c2e63378349b74
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.