Instructions to use yujiepan/clip-vit-tiny-random-patch14-336 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yujiepan/clip-vit-tiny-random-patch14-336 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="yujiepan/clip-vit-tiny-random-patch14-336") 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("yujiepan/clip-vit-tiny-random-patch14-336") model = AutoModelForZeroShotImageClassification.from_pretrained("yujiepan/clip-vit-tiny-random-patch14-336", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from yujiepan/clip-vit-tiny-random-patch14-336: direct link, hf CLI and curl.
- Browser
- Download file 824 kB
-
https://huggingface.co/yujiepan/clip-vit-tiny-random-patch14-336/resolve/main/model.safetensors
- Command line
-
hf download hf://yujiepan/clip-vit-tiny-random-patch14-336/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/yujiepan/clip-vit-tiny-random-patch14-336/resolve/main/model.safetensors
824 kB
- Xet hash:
- 2b8893d37caa7f1c20e417504eb0e8ad8fa0acdbd82ea95ebcd0570bc08e8e13
- Size of remote file:
- 824 kB
- SHA256:
- 595617188b2fe2500890e9079cba4139d68d187698984009092d474b6ab4f4c7
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