Instructions to use nvidia/C-RADIOv4-SO400M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/C-RADIOv4-SO400M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nvidia/C-RADIOv4-SO400M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/C-RADIOv4-SO400M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download c-radio_v4-so400m_half.pth.tar from nvidia/C-RADIOv4-SO400M: direct link, hf CLI and curl.
- Browser
- Download file 1.21 GB
-
https://huggingface.co/nvidia/C-RADIOv4-SO400M/resolve/main/c-radio_v4-so400m_half.pth.tar
- Command line
-
hf download hf://nvidia/C-RADIOv4-SO400M/c-radio_v4-so400m_half.pth.tar
-
curl -L -o c-radio_v4-so400m_half.pth.tar https://huggingface.co/nvidia/C-RADIOv4-SO400M/resolve/main/c-radio_v4-so400m_half.pth.tar
1.21 GB
- Xet hash:
- e632fcdf0707a29780e13a02bd2a301ac36d02479a0cc63058fba5f4aac44e2f
- Size of remote file:
- 1.21 GB
- SHA256:
- d02697ede20f2716c4db12a9d3dab1c5a9b47d15a16cdcd46a69d08062b77aaf
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