Instructions to use google/siglip2-so400m-patch16-naflex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/siglip2-so400m-patch16-naflex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/siglip2-so400m-patch16-naflex") 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("google/siglip2-so400m-patch16-naflex") model = AutoModelForZeroShotImageClassification.from_pretrained("google/siglip2-so400m-patch16-naflex") - Notebooks
- Google Colab
- Kaggle
QORA-Vision (Image) - Native Rust Image Encoder based on SigLIP 2
#7
by drdraq - opened
Pure Rust image understanding engine based on SigLIP 2. Zero-shot image classification, image embeddings, and image-text similarity. No Python runtime, no CUDA, no external dependencies.
Try: https://huggingface.co/qoranet/QORA-Vision-Image
Zero-shot classification (fast, from binary)
qora-vision.exe siglip --load model.qora-vision --image photo.jpg --labels "cat,dog,bird,car"
Image-text similarity
qora-vision.exe siglip --load model.qora-vision --image photo.jpg --text "a photo of a sunset"
Image embedding only
qora-vision.exe siglip --load model.qora-vision --image photo.jpg
Load from safetensors (slow, first time)
qora-vision.exe siglip --model-path ../SigLIP2/ --image photo.jpg --labels "cat,dog,bird,car"
Save binary for fast loading
qora-vision.exe siglip --model-path ../SigLIP2/ --save model.qora-vision