Instructions to use hf-tiny-model-private/tiny-random-ChineseCLIPModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/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-tiny-model-private/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-tiny-model-private/tiny-random-ChineseCLIPModel") model = AutoModelForZeroShotImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-ChineseCLIPModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8e724154517e587a26322ad764f8255e28b4a512124dd913b1b0cd24ce1b1014
- Size of remote file:
- 557 kB
- SHA256:
- 7f5c41e3efe2aac1a34886bf5727550620f0b93a15b733246cc7942a0b4cb39c
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