Instructions to use timm/vit_base_patch16_radio_v3.c_radio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_base_patch16_radio_v3.c_radio with timm:
import timm model = timm.create_model("hf-hub:timm/vit_base_patch16_radio_v3.c_radio", pretrained=True) - Transformers
How to use timm/vit_base_patch16_radio_v3.c_radio with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_base_patch16_radio_v3.c_radio")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("timm/vit_base_patch16_radio_v3.c_radio") model = AutoModel.from_pretrained("timm/vit_base_patch16_radio_v3.c_radio", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download conversion.json from timm/vit_base_patch16_radio_v3.c_radio: direct link, hf CLI and curl.
- Browser
- Download file 424 Bytes
-
https://huggingface.co/timm/vit_base_patch16_radio_v3.c_radio/resolve/main/conversion.json
- Command line
-
hf download hf://timm/vit_base_patch16_radio_v3.c_radio/conversion.json
-
curl -L -o conversion.json https://huggingface.co/timm/vit_base_patch16_radio_v3.c_radio/resolve/main/conversion.json
424 Bytes
| { | |
| "source_repo": "nvidia/C-RADIOv3-B", | |
| "source_revision": "44653a0482cf460bb4f12595fc3cc3dfecc403d1", | |
| "source_filename": "model.safetensors", | |
| "license_source": "https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf", | |
| "license_sha256": "396d7de220f6b0e6fcfe33836f1b99a9943769b97c56330444f8efe1b441f607", | |
| "timm_version": "1.0.31.dev0", | |
| "torch_version": "2.9.1+cu130" | |
| } | |