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:
- a36c3fafeb4fbc9daa19fb843163fb74f516c7f2d4839625cf5bc5ca26eb88f2
- Size of remote file:
- 723 kB
- SHA256:
- 7714fee94709ec380e321fd721c26b740f7f1e6740b4e87e54ead6d42d3daee0
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