Instructions to use nvidia/C-RADIOv2-B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/C-RADIOv2-B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="nvidia/C-RADIOv2-B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/C-RADIOv2-B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9aee2584a3c2b4ad9c00059923fa0e18f7b65a6c1d61752609ec132c1a1c1a4f
- Size of remote file:
- 393 MB
- SHA256:
- 9851698e8550f40f45c3e4ad8f9e32ef7987518d6667bb1c0eb66f99c72a13aa
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