Instructions to use HuggingFaceM4/tiny-random-siglip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceM4/tiny-random-siglip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="HuggingFaceM4/tiny-random-siglip", trust_remote_code=True) 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("HuggingFaceM4/tiny-random-siglip", trust_remote_code=True) model = AutoModelForZeroShotImageClassification.from_pretrained("HuggingFaceM4/tiny-random-siglip", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c89cd1dc82a982a154029cddbcc46032ba1a355e0e04547e507ea8b762426a6
- Size of remote file:
- 25.4 MB
- SHA256:
- 631e356c6c4b575d065319d18bac52cb783039f71b4f7337f4704758f3f962cd
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