Instructions to use CollectionStudio/siglip2-base-patch16-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CollectionStudio/siglip2-base-patch16-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="CollectionStudio/siglip2-base-patch16-256") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CollectionStudio/siglip2-base-patch16-256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from CollectionStudio/siglip2-base-patch16-256: direct link, hf CLI and curl.
- Browser
- Download file 1.5 GB
-
https://huggingface.co/CollectionStudio/siglip2-base-patch16-256/resolve/main/model.safetensors
- Command line
-
hf download hf://CollectionStudio/siglip2-base-patch16-256/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/CollectionStudio/siglip2-base-patch16-256/resolve/main/model.safetensors
1.5 GB
- Xet hash:
- 9be0ac750d3103ed7f1412112b91d42ccc241bfa4999a3ee6872a4f519a46e4d
- Size of remote file:
- 1.5 GB
- SHA256:
- 6125cacc01fa93bdc98a0c5101cefcd69b2ed1f8ab4f38d86f4ad5984f5dc863
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