Instructions to use arampacha/clip-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arampacha/clip-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="arampacha/clip-test") 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("arampacha/clip-test") model = AutoModelForZeroShotImageClassification.from_pretrained("arampacha/clip-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from arampacha/clip-test: direct link, hf CLI and curl.
- Browser
- Download file 605 MB
-
https://huggingface.co/arampacha/clip-test/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://arampacha/clip-test/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/arampacha/clip-test/resolve/main/pytorch_model.bin
605 MB
- Xet hash:
- 5a1867fd420c7b851091fb8167ffe11fb84480696c2686ea37b5fde527dccf76
- Size of remote file:
- 605 MB
- SHA256:
- e8fce7cc0dea8b7d13339a0b63cd58f49ee938200fa96a832b64c7f91a21c0b7
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