Instructions to use hf-internal-testing/tiny-random-ChineseCLIPModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ChineseCLIPModel 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-ChineseCLIPModel") 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-ChineseCLIPModel") model = AutoModelForZeroShotImageClassification.from_pretrained("hf-internal-testing/tiny-random-ChineseCLIPModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 731195f8eb95ee3394c313155f8c14b51d55bfff9cd3be042be0969cd3871bbd
- Size of remote file:
- 594 kB
- SHA256:
- 34748e155dc9abd4cad9c7e6c92a52c9340862e246c31132231a58945e558f63
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