Instructions to use lewtun/tiny-clip-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lewtun/tiny-clip-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="lewtun/tiny-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("lewtun/tiny-clip-test") model = AutoModelForZeroShotImageClassification.from_pretrained("lewtun/tiny-clip-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding ONNX file of this model
Verified signatureBeep boop I am the [ONNX export bot 🤖🏎️](https://huggingface.co/spaces/onnx/export). On behalf of [RainbowPiBubbles](https://huggingface.co/RainbowPiBubbles), I would like to add to this repository the model converted to ONNX. What is ONNX? It stands for "Open Neural Network Exchange", and is the most commonly used open standard for machine learning interoperability. You can find out more at [onnx.ai](https://onnx.ai/)! The exported ONNX model can be then be consumed by various backends as TensorRT or TVM, or simply be used in a few lines with 🤗 Optimum through ONNX Runtime, check out how [here](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/models)!0e24684
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