Instructions to use nvidia/C-RADIOv2-g with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/C-RADIOv2-g with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="nvidia/C-RADIOv2-g", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/C-RADIOv2-g", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download c-radio_v2-g_half.pth.tar from nvidia/C-RADIOv2-g: direct link, hf CLI and curl.
- Browser
- Download file 2.48 GB
-
https://huggingface.co/nvidia/C-RADIOv2-g/resolve/main/c-radio_v2-g_half.pth.tar
- Command line
-
hf download hf://nvidia/C-RADIOv2-g/c-radio_v2-g_half.pth.tar
-
curl -L -o c-radio_v2-g_half.pth.tar https://huggingface.co/nvidia/C-RADIOv2-g/resolve/main/c-radio_v2-g_half.pth.tar
2.48 GB
- Xet hash:
- 42841c1897d6c48ac75e6cbd5a5b553ce2b2e3fa2160b6b27a6ae990a8f2da05
- Size of remote file:
- 2.48 GB
- SHA256:
- 587b4c1c6abcb64bce7377c1bed91a8ca524f18e0c90cb603d6b0439b1b59349
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.