Instructions to use timm/vit_base_patch16_siglip_384.v2_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_384.v2_webli with timm:
import timm model = timm.create_model("hf_hub:timm/vit_base_patch16_siglip_384.v2_webli", pretrained=True) - Transformers
How to use timm/vit_base_patch16_siglip_384.v2_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_384.v2_webli")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_base_patch16_siglip_384.v2_webli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/vit_base_patch16_siglip_384.v2_webli: direct link, hf CLI and curl.
- Browser
- Download file 373 MB
-
https://huggingface.co/timm/vit_base_patch16_siglip_384.v2_webli/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/vit_base_patch16_siglip_384.v2_webli/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/vit_base_patch16_siglip_384.v2_webli/resolve/main/pytorch_model.bin
373 MB
- Xet hash:
- 5ae507fca5904353a96d94fc6026d189fd174b7c31ad1a1a453266942d510809
- Size of remote file:
- 373 MB
- SHA256:
- 6fc5c5baebec562d33371370112e84d059aa7482c4726d9fb45d206420491d45
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