Instructions to use timm/vit_base_patch16_siglip_gap_224.webli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_base_patch16_siglip_gap_224.webli with timm:
import timm model = timm.create_model("hf-hub:timm/vit_base_patch16_siglip_gap_224.webli", pretrained=True) - Transformers
How to use timm/vit_base_patch16_siglip_gap_224.webli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_base_patch16_siglip_gap_224.webli")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_base_patch16_siglip_gap_224.webli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/vit_base_patch16_siglip_gap_224.webli: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/timm/vit_base_patch16_siglip_gap_224.webli/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/vit_base_patch16_siglip_gap_224.webli/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/vit_base_patch16_siglip_gap_224.webli/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- b192f391bb0af8927837d9aca46253082699c7ec4dbfdac70fad3884952ec60e
- Size of remote file:
- 343 MB
- SHA256:
- 2338509effd7c1e50cb5c7150175cd42063abaf1f2ecf87c9c9b2147b1f0ee15
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