Instructions to use openai/clip-vit-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/clip-vit-base-patch32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch32") 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("openai/clip-vit-base-patch32") model = AutoModelForZeroShotImageClassification.from_pretrained("openai/clip-vit-base-patch32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from openai/clip-vit-base-patch32: direct link, hf CLI and curl.
- Browser
- Download file 605 MB
-
https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://openai/clip-vit-base-patch32/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/pytorch_model.bin
605 MB
- Xet hash:
- b9309dbadde00679b71e6cfd0d387b79e0b29d222133240986eaf9b4e3cd9e62
- Size of remote file:
- 605 MB
- SHA256:
- a63082132ba4f97a80bea76823f544493bffa8082296d62d71581a4feff1576f
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