Instructions to use google/siglip2-giant-opt-patch16-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/siglip2-giant-opt-patch16-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/siglip2-giant-opt-patch16-384") 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-giant-opt-patch16-384", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add image-text-to-text pipeline tag
#2
by nielsr HF Staff - opened
This PR updates the model card metadata to use the image-text-to-text pipeline tag. This tag better reflects the model's multimodal capabilities, including image captioning and visual question answering, as demonstrated in the provided examples and described in the paper. This change improves the model's discoverability on the Hub for users seeking vision-language models.