Instructions to use Veritone/siglip2-so400m-patch16-384-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Veritone/siglip2-so400m-patch16-384-vision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Veritone/siglip2-so400m-patch16-384-vision") 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("Veritone/siglip2-so400m-patch16-384-vision") model = AutoModelForZeroShotImageClassification.from_pretrained("Veritone/siglip2-so400m-patch16-384-vision", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload vision model tensors
Browse files- config.json +2 -1
config.json
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"num_attention_heads": 16,
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"num_hidden_layers": 27,
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"projection_size": 1152,
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"vocab_size": 256000
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},
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"transformers_version": "4.49.0.dev0",
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"vision_config": {
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"num_attention_heads": 16,
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"num_hidden_layers": 27,
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"projection_size": 1152,
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"vocab_size": 256000,
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"pad_token_id": 0
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},
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"transformers_version": "4.49.0.dev0",
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"vision_config": {
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