Instructions to use openai/clip-vit-base-patch16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/clip-vit-base-patch16 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-patch16") 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-base-patch16") model = AutoModelForZeroShotImageClassification.from_pretrained("openai/clip-vit-base-patch16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from openai/clip-vit-base-patch16: direct link, hf CLI and curl.
- Browser
- Download file 599 MB
-
https://huggingface.co/openai/clip-vit-base-patch16/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://openai/clip-vit-base-patch16/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/openai/clip-vit-base-patch16/resolve/main/pytorch_model.bin
599 MB
- Xet hash:
- 3fe4c151abe794267c6045a80eebe81bd266e0a9dfd63a386da7eefa50b38c43
- Size of remote file:
- 599 MB
- SHA256:
- ec89c7b09c749a60aae3c9cd910516f24b58214a7df060b48962d14c469cfbf0
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