Instructions to use google/siglip2-so400m-patch14-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/siglip2-so400m-patch14-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/siglip2-so400m-patch14-384") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("google/siglip2-so400m-patch14-384", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Fix model_max_length
#4
by fancyfeast - opened
- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
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@@ -2008,7 +2008,7 @@
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"model_input_names": [
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"input_ids"
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],
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-
"model_max_length":
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"pad_token": "<pad>",
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"padding_side": "right",
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"processor_class": "SiglipProcessor",
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"model_input_names": [
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"input_ids"
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],
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+
"model_max_length": 64,
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"pad_token": "<pad>",
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"padding_side": "right",
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| 2014 |
"processor_class": "SiglipProcessor",
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