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", device_map="auto") 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:
- 3ee26d1a02f17adb34b1b3e8141d7c6117d15092dbffaa54f67b28967c0394a0
- Size of remote file:
- 4.34 MB
- SHA256:
- 745cff8f7611050c51c66a7fa5b77c9c6f987cd48fc213ff5321ed550d2b2aeb
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