Instructions to use hf-internal-testing/tiny-random-CLIPModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-CLIPModel 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-CLIPModel") 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-CLIPModel") model = AutoModelForZeroShotImageClassification.from_pretrained("hf-internal-testing/tiny-random-CLIPModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 45fbfadee6a6f5149c6ad77a9f7e4b41527449dfa91dc29938c5ccc4d62d7b87
- Size of remote file:
- 768 kB
- SHA256:
- 3c1108337f06948784556c29d0c9f48c4f9b910d7222dcf4b314dd2fcc75284c
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