Instructions to use llm-jp/waon-siglip2-base-patch16-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llm-jp/waon-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="llm-jp/waon-siglip2-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("llm-jp/waon-siglip2-base-patch16-256") model = AutoModelForZeroShotImageClassification.from_pretrained("llm-jp/waon-siglip2-base-patch16-256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1390f677b6e50bfd89fb4abf3027670314124cd59faa8d2c3214eb0b42221856
- Size of remote file:
- 1.5 GB
- SHA256:
- b6a3158ec64ec51f23bed222cce7897837e30f33dfa3b02c10e641a333c2bc86
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