Instructions to use nvidia/RADIO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/RADIO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="nvidia/RADIO", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/RADIO", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download radio_v2.5-h-norm.pth.tar from nvidia/RADIO: direct link, hf CLI and curl.
- Browser
- Download file 2.29 GB
-
https://huggingface.co/nvidia/RADIO/resolve/main/radio_v2.5-h-norm.pth.tar
- Command line
-
hf download hf://nvidia/RADIO/radio_v2.5-h-norm.pth.tar
-
curl -L -o radio_v2.5-h-norm.pth.tar https://huggingface.co/nvidia/RADIO/resolve/main/radio_v2.5-h-norm.pth.tar
2.29 GB
- Xet hash:
- 3b60b9d63b14dac077d488984896e0256bc46fcaffd70ca8c292bc1d3da98a08
- Size of remote file:
- 2.29 GB
- SHA256:
- 06d5b0bfda0e94deda2ad22c82fab9f3bf2a8b0d66b3b9f4137d5a85cea5d356
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.