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:
- f4e126a28e6bb9c0a83d67909fe4f5276c54066927ce3f5dd5b4a9d59ae5a5b3
- Size of remote file:
- 557 kB
- SHA256:
- 7b882c0e27a06bb8b0429baef118156d924f9dba9e78674d88d7648dde6dccf5
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