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, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f659829424abfd2fca287739688e07ea05e19aab7393476f2e6edc464b5608d7
- Size of remote file:
- 4.64 GB
- SHA256:
- fa6e741d60c99c87d8be4f74439daaadf1eb831bf78d4cfbe1e97ce672204bd1
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