Instructions to use hf-tiny-model-private/tiny-random-CLIPModel 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-CLIPModel 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-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-tiny-model-private/tiny-random-CLIPModel") model = AutoModelForZeroShotImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-CLIPModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 49e85a7c2b9398867ff1ca3a1d2fb89ecc1adc49943b98a06fc507847c8ae244
- Size of remote file:
- 723 kB
- SHA256:
- 0bc2e75401e83056331bc2d90594c954eb6b9ba20f00058ecd583cc985864761
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