Instructions to use google/siglip-base-patch16-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/siglip-base-patch16-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/siglip-base-patch16-256") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-256") model = AutoModelForZeroShotImageClassification.from_pretrained("google/siglip-base-patch16-256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from google/siglip-base-patch16-256: direct link, hf CLI and curl.
- Browser
- Download file 813 MB
-
https://huggingface.co/google/siglip-base-patch16-256/resolve/main/model.safetensors
- Command line
-
hf download hf://google/siglip-base-patch16-256/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/google/siglip-base-patch16-256/resolve/main/model.safetensors
813 MB
- Xet hash:
- a67880a745240c25d202962c6b99845f48533e85f0a7f6082c01a3bad575bb84
- Size of remote file:
- 813 MB
- SHA256:
- f0cee7c815135c44a515eff72ab3040499744920442bc25567cd04efc93f8f65
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