Instructions to use OFA-Sys/chinese-clip-vit-large-patch14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OFA-Sys/chinese-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="OFA-Sys/chinese-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("OFA-Sys/chinese-clip-vit-large-patch14") model = AutoModelForZeroShotImageClassification.from_pretrained("OFA-Sys/chinese-clip-vit-large-patch14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from OFA-Sys/chinese-clip-vit-large-patch14: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/OFA-Sys/chinese-clip-vit-large-patch14/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://OFA-Sys/chinese-clip-vit-large-patch14/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/OFA-Sys/chinese-clip-vit-large-patch14/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- 782aeed20303b485b1b08f1004f33decbf8ce9ea94307edd32904227f3551c92
- Size of remote file:
- 1.63 GB
- SHA256:
- f35bc7ad4db5a47e87f4cef6cc4c643a7190f64a66927ff7aec4cd186ddcd4d2
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