Instructions to use nvidia/C-RADIOv2-g with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/C-RADIOv2-g with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="nvidia/C-RADIOv2-g", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/C-RADIOv2-g", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42841c1897d6c48ac75e6cbd5a5b553ce2b2e3fa2160b6b27a6ae990a8f2da05
- Size of remote file:
- 2.48 GB
- SHA256:
- 587b4c1c6abcb64bce7377c1bed91a8ca524f18e0c90cb603d6b0439b1b59349
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