Instructions to use CollectionStudio/siglip2-base-patch16-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CollectionStudio/siglip2-base-patch16-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="CollectionStudio/siglip2-base-patch16-384") 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-patch16-384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from CollectionStudio/siglip2-base-patch16-384: direct link, hf CLI and curl.
- Browser
- Download file 1.5 GB
-
https://huggingface.co/CollectionStudio/siglip2-base-patch16-384/resolve/main/model.safetensors
- Command line
-
hf download hf://CollectionStudio/siglip2-base-patch16-384/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/CollectionStudio/siglip2-base-patch16-384/resolve/main/model.safetensors
1.5 GB
- Xet hash:
- a6f9b9f4fff18b89180870216d367fd0bb367e15b75d89dea1e548a51004d897
- Size of remote file:
- 1.5 GB
- SHA256:
- ed72c0ace85020ae610fc817c2538b9cae5a477b012a50859c60af5b3ad30857
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