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 training_args.bin from arampacha/clip-test: direct link, hf CLI and curl.
- Browser
- Download file 3.12 kB
-
https://huggingface.co/arampacha/clip-test/resolve/main/training_args.bin
- Command line
-
hf download hf://arampacha/clip-test/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/arampacha/clip-test/resolve/main/training_args.bin
3.12 kB
- Xet hash:
- 27f45e45128a2ba54b31d0d61bb1f7e22a54ccc818a83e0fa01636f25a06e068
- Size of remote file:
- 3.12 kB
- SHA256:
- 9531854fe1e051e889f55efda1f43594028b9ce688f2a64de220ee0428d7c176
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