Instructions to use mattmdjaga/clip-vit-base-patch32_handler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mattmdjaga/clip-vit-base-patch32_handler with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="mattmdjaga/clip-vit-base-patch32_handler") 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("mattmdjaga/clip-vit-base-patch32_handler") model = AutoModelForZeroShotImageClassification.from_pretrained("mattmdjaga/clip-vit-base-patch32_handler") - Notebooks
- Google Colab
- Kaggle
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README.md
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Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found [here](https://github.com/openai/CLIP/blob/main/model-card.md).
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I mattmdjaga added a handler to this model which seemed to be needed when deploying via inference endpoints.
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## Model Details
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Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found [here](https://github.com/openai/CLIP/blob/main/model-card.md).
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**I mattmdjaga added a handler to this model which seemed to be needed when deploying via inference endpoints.**
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## Model Details
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