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, dtype="auto") - Notebooks
- Google Colab
- Kaggle
add C-RADIOv2-g checkpoint (#5)
Browse files- Add C-RADIOv2-g Checkpoint (1b48638eb1a1c13e8c1f253a58062f703c3b1b60)
c-radio_v2-g_half.pth.tar
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version https://git-lfs.github.com/spec/v1
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oid sha256:587b4c1c6abcb64bce7377c1bed91a8ca524f18e0c90cb603d6b0439b1b59349
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size 2479406118
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