Instructions to use hf-internal-testing/tiny-random-onnx-clip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-onnx-clip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="hf-internal-testing/tiny-random-onnx-clip") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-onnx-clip") model = AutoModelForZeroShotImageClassification.from_pretrained("hf-internal-testing/tiny-random-onnx-clip") - Notebooks
- Google Colab
- Kaggle
[Awaiting approval] Upload ONNX weights
#1
by Xenova HF Staff - opened
- onnx/model.onnx +3 -0
onnx/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:e37bfb0ca7bf9619683f5e66c17dec45a562962200775b88060bc076ea68c40d
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size 6954319
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