Instructions to use timm/convnext_base.dinov3_lvd1689m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convnext_base.dinov3_lvd1689m with timm:
import timm model = timm.create_model("hf-hub:timm/convnext_base.dinov3_lvd1689m", pretrained=True) - Transformers
How to use timm/convnext_base.dinov3_lvd1689m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/convnext_base.dinov3_lvd1689m")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("timm/convnext_base.dinov3_lvd1689m") model = AutoModel.from_pretrained("timm/convnext_base.dinov3_lvd1689m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/convnext_base.dinov3_lvd1689m: direct link, hf CLI and curl.
- Browser
- Download file 350 MB
-
https://huggingface.co/timm/convnext_base.dinov3_lvd1689m/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/convnext_base.dinov3_lvd1689m/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/convnext_base.dinov3_lvd1689m/resolve/main/pytorch_model.bin
350 MB
- Xet hash:
- 5e72f73e431291f2794446ed14077b452ad65dda3e16fa9be689cc5b9d0824ef
- Size of remote file:
- 350 MB
- SHA256:
- c16dbb122578e571ec9604c5d7035df0ae84acbf5e6343f920f657b527b66433
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