Instructions to use hf-internal-testing/tiny-random-SiglipModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SiglipModel 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-SiglipModel") 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-SiglipModel") model = AutoModelForZeroShotImageClassification.from_pretrained("hf-internal-testing/tiny-random-SiglipModel") - Notebooks
- Google Colab
- Kaggle
Update tiny models for SiglipModel
Browse files- config.json +1 -1
- model.safetensors +1 -1
config.json
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"num_hidden_layers": 2
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"transformers_version": "4.
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"vision_config": {
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"attention_dropout": 0.1,
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"dropout": 0.1,
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"transformers_version": "4.38.0.dev0",
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"vision_config": {
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"dropout": 0.1,
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model.safetensors
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size 4342092
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