Instructions to use openai/clip-vit-large-patch14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/clip-vit-large-patch14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="openai/clip-vit-large-patch14") 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("openai/clip-vit-large-patch14") model = AutoModelForZeroShotImageClassification.from_pretrained("openai/clip-vit-large-patch14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from openai/clip-vit-large-patch14: direct link, hf CLI and curl.
- Browser
- Download file 1.71 GB
-
https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/model.safetensors
- Command line
-
hf download hf://openai/clip-vit-large-patch14/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/model.safetensors
1.71 GB
- Xet hash:
- 9046d5fe172d35ca65c0140b3d9c638d31b2714cc17049ee40fcf887ab0e076a
- Size of remote file:
- 1.71 GB
- SHA256:
- a2bf730a0c7debf160f7a6b50b3aaf3703e7e88ac73de7a314903141db026dcb
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