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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/E-RADIO", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nvidia/E-RADIO: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/nvidia/E-RADIO/resolve/f134378c91fb81b1a27a806d4d16533a4b5047a8/pytorch_model.bin
- Command line
-
hf download hf://nvidia/E-RADIO@f134378c91fb81b1a27a806d4d16533a4b5047a8/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nvidia/E-RADIO/resolve/f134378c91fb81b1a27a806d4d16533a4b5047a8/pytorch_model.bin
1.11 GB
- Xet hash:
- 9a4dac7aa4b7ef1c741a2c125949d7f152f1875f62546ba0281c4dd2172ed9ce
- Size of remote file:
- 1.11 GB
- SHA256:
- 3840092575224b5ff90adf3b7970a5a5e379f8988241ee8145969e27c32c17e7
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