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:
- 652952bd66b3abe41bf5ec0a3065c49c46b5f2560ccffe25bab90f64b3f7bae7
- Size of remote file:
- 579 kB
- SHA256:
- 4d0ce4dd8f7b4bd5fd35cec79eb5bfdd153357d0dea5a7b109a63294468e64d8
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