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:
- eecbf403e92ce60cb1acf58ddafa3b5e0a29765814feaf42af63d40d5303fd11
- Size of remote file:
- 100 MB
- SHA256:
- dd194c9ff87c82a0e5f4d3c0bf2ebc2e2d58cc69c6150d3b8a805438d77e0f4b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.