Instructions to use nvidia/C-RADIOv4-H with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/C-RADIOv4-H with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nvidia/C-RADIOv4-H", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/C-RADIOv4-H", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c540b73a3e1940e5392286bb288ea5209d9ce2797bc09becc9d61c68071bb762
- Size of remote file:
- 2.61 GB
- SHA256:
- 851f283468118830a08e70173f1ab9a30f613c704958fc0b3c8e638aef738f81
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