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:
- a1feb0894ed48f743fe34d1ebb6cfe5d7d33d7e6869eabc61f124e358ae1b051
- Size of remote file:
- 1.68 GB
- SHA256:
- bace44df72e750bc8555ea6979cc19d1a87e12ade89582edfe090513d5d6aab9
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