cstr/siglip-large-256-GGUF

SigLIP ViT-L/16 Vision Encoder 256px (GGUF)

GGUF conversion of google/siglip-large-patch16-256 for use with CrispEmbed.

• Architecture: SigLIP ViT-L/16 vision encoder
• Parameters: 304M
• Output: 1024-dimensional L2-normalized embeddings
• Input: 256x256 RGB image with SigLIP normalization (mean=0.5, std=0.5)
• Size: ~1.2 GB
• Source: google/siglip-large-patch16-256

Usage

# Embed a single image
crispembed -m siglip-large-256 --image photo.jpg

# Batch processing
crispembed -m siglip-large-256 --image-dir ./photos/ --output embeddings.bin

About SigLIP

SigLIP replaces CLIP's softmax contrastive loss with a sigmoid loss, enabling better scaling and stronger performance on retrieval benchmarks. It uses a simpler normalization scheme (mean=0.5, std=0.5) compared to CLIP's ImageNet statistics.

Notes

• All output embeddings are L2-normalized.
• This is a GGUF conversion; weights are numerically equivalent to the original HuggingFace model.