Instructions to use Bingsu/clip-vit-base-patch32-ko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bingsu/clip-vit-base-patch32-ko with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Bingsu/clip-vit-base-patch32-ko") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("Bingsu/clip-vit-base-patch32-ko") model = AutoModelForZeroShotImageClassification.from_pretrained("Bingsu/clip-vit-base-patch32-ko", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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