Instructions to use OFA-Sys/chinese-clip-vit-base-patch16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OFA-Sys/chinese-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="OFA-Sys/chinese-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("OFA-Sys/chinese-clip-vit-base-patch16") model = AutoModelForZeroShotImageClassification.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from OFA-Sys/chinese-clip-vit-base-patch16: direct link, hf CLI and curl.
- Browser
- Download file 753 MB
-
https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://OFA-Sys/chinese-clip-vit-base-patch16/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16/resolve/main/pytorch_model.bin
753 MB
- Xet hash:
- 82d798fb494e387d91eb24b425b1c4c5ba52405a20336afb3ddbaabecc445d67
- Size of remote file:
- 753 MB
- SHA256:
- 7b7b583c210c867410bc6bdb8a55fe14eec62999e0a9ea31ff222dc501f9cfbe
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