Instructions to use nvidia/E-RADIO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/E-RADIO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nvidia/E-RADIO", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/E-RADIO", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6bbf833125615aac1ac955236da2bf439c0ce9e4ccec848eea5a54cd71c317bd
- Size of remote file:
- 1.61 GB
- SHA256:
- 8e9efb59a1543a3e3eaea4d5d11dd7be4ae0732395830adbfc75d6f5079dfdc6
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