Instructions to use hf-internal-testing/tiny-random-SiglipModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SiglipModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="hf-internal-testing/tiny-random-SiglipModel") 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("hf-internal-testing/tiny-random-SiglipModel") model = AutoModelForZeroShotImageClassification.from_pretrained("hf-internal-testing/tiny-random-SiglipModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f4648a51e274f8a85fec3375d7d9f05d61be95dddb175e84d85a83917151676c
- Size of remote file:
- 4.34 MB
- SHA256:
- 8254c30bf21355aff9e52c2442372c2dc78343eb27666881a9aa1f3dec65f293
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