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
Fix config
#4
by Xenova HF Staff - opened
- config.json +1 -1
config.json
CHANGED
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@@ -146,7 +146,7 @@
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"patch_size": 2,
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"prefix": null,
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"problem_type": null,
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-
"projection_dim":
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"pruned_heads": {},
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"remove_invalid_values": false,
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"repetition_penalty": 1.0,
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"patch_size": 2,
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"prefix": null,
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"problem_type": null,
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+
"projection_dim": 64,
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"pruned_heads": {},
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"remove_invalid_values": false,
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"repetition_penalty": 1.0,
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