Instructions to use Veritone/siglip2-base-patch16-256-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Veritone/siglip2-base-patch16-256-vision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Veritone/siglip2-base-patch16-256-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-base-patch16-256-vision") model = AutoModelForZeroShotImageClassification.from_pretrained("Veritone/siglip2-base-patch16-256-vision", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Fix model.safetensors: restore vision tensors (file held only logit scalars)
#1
by dietmar-veri - opened
- model.safetensors +2 -2
model.safetensors
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