Instructions to use timm/vit_small_plus_patch16_dinov3.lvd1689m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_small_plus_patch16_dinov3.lvd1689m with timm:
import timm model = timm.create_model("hf-hub:timm/vit_small_plus_patch16_dinov3.lvd1689m", pretrained=True) - Transformers
How to use timm/vit_small_plus_patch16_dinov3.lvd1689m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_small_plus_patch16_dinov3.lvd1689m")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_small_plus_patch16_dinov3.lvd1689m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/vit_small_plus_patch16_dinov3.lvd1689m: direct link, hf CLI and curl.
- Browser
- Download file 115 MB
-
https://huggingface.co/timm/vit_small_plus_patch16_dinov3.lvd1689m/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/vit_small_plus_patch16_dinov3.lvd1689m/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/vit_small_plus_patch16_dinov3.lvd1689m/resolve/main/pytorch_model.bin
115 MB
- Xet hash:
- 6567e67575a492b623c872d4c9dc52cd5f3a6799e90a694a7dc73eaf3182710b
- Size of remote file:
- 115 MB
- SHA256:
- a9f385cfcbeade38c303f6958ecb91fdad6c00197098ad5e2b0a7e0618e357a8
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