Instructions to use hf-internal-testing/tiny-random-CLIPModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-CLIPModel 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-CLIPModel") 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-CLIPModel") model = AutoModelForZeroShotImageClassification.from_pretrained("hf-internal-testing/tiny-random-CLIPModel") - Notebooks
- Google Colab
- Kaggle
Upload ONNX weights (#5)
Browse files- Upload ONNX weights (784ae73a3fe7bfe6474e9c5d062f0fff109e09a8)
- onnx/model.onnx +3 -0
- onnx/text_model.onnx +3 -0
- onnx/vision_model.onnx +3 -0
onnx/model.onnx
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onnx/text_model.onnx
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onnx/vision_model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:dbd508ae0c9366acceff5e046f860890882a2aa1d9c2518d63cdf281f838dfd3
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size 272263
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