Instructions to use timm/vit_large_patch16_siglip_gap_256.webli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_large_patch16_siglip_gap_256.webli with timm:
import timm model = timm.create_model("hf-hub:timm/vit_large_patch16_siglip_gap_256.webli", pretrained=True) - Transformers
How to use timm/vit_large_patch16_siglip_gap_256.webli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_large_patch16_siglip_gap_256.webli")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("timm/vit_large_patch16_siglip_gap_256.webli") model = AutoModel.from_pretrained("timm/vit_large_patch16_siglip_gap_256.webli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/vit_large_patch16_siglip_gap_256.webli: direct link, hf CLI and curl.
- Browser
- Download file 1.21 GB
-
https://huggingface.co/timm/vit_large_patch16_siglip_gap_256.webli/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/vit_large_patch16_siglip_gap_256.webli/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/vit_large_patch16_siglip_gap_256.webli/resolve/main/pytorch_model.bin
1.21 GB
- Xet hash:
- b215ff78374f38bdd6cb393a5f025c525aa5db1cf277de920445459dd151edff
- Size of remote file:
- 1.21 GB
- SHA256:
- e841f59369c4c5d4bad7d596355a74d13cab05746ceef07e4e9132b544edea80
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