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 flax_model.msgpack from openai/clip-vit-base-patch16: direct link, hf CLI and curl.
- Browser
- Download file 598 MB
-
https://huggingface.co/openai/clip-vit-base-patch16/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://openai/clip-vit-base-patch16/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/openai/clip-vit-base-patch16/resolve/main/flax_model.msgpack
598 MB
- Xet hash:
- c33261d38caf2fe32f70bc3e9b13281e544e8b419a3f3a379e970e719805a62e
- Size of remote file:
- 598 MB
- SHA256:
- eb01703b5ecb458909b5dadbbadd929f7096782dda1f879273bbd38a2c14fc66
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