Instructions to use nvidia/C-RADIO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/C-RADIO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nvidia/C-RADIO", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/C-RADIO", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c54a5fca6f879e49b85e97d07315ecf67422204dbf3e940563251386a3e98ac
- Size of remote file:
- 2.61 GB
- SHA256:
- d22d2749203ab655c495f63f5903c9d58931dfca5e32703d0ca99e886e3a647b
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