Instructions to use team-lucid/siglip-base-patch16-ko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use team-lucid/siglip-base-patch16-ko with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="team-lucid/siglip-base-patch16-ko") 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 AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("team-lucid/siglip-base-patch16-ko") model = AutoModelForZeroShotImageClassification.from_pretrained("team-lucid/siglip-base-patch16-ko", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from team-lucid/siglip-base-patch16-ko: direct link, hf CLI and curl.
- Browser
- Download file 563 MB
-
https://huggingface.co/team-lucid/siglip-base-patch16-ko/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://team-lucid/siglip-base-patch16-ko/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/team-lucid/siglip-base-patch16-ko/resolve/main/flax_model.msgpack
563 MB
- Xet hash:
- 0c802cc3b7d3d1c92cce33cf68b7cb86109cc5d28b6a501cf3c35d5c5b0adfc8
- Size of remote file:
- 563 MB
- SHA256:
- d233d0a234e78f3d35555cea88e3684a80b8823f97d1fcd0110cc9cd05327e4c
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