cstr/clip-vit-base-patch16-GGUF

CLIP ViT-B/16 Vision Encoder (GGUF)

GGUF conversion of openai/clip-vit-base-patch16 for use with CrispEmbed.

• Architecture: CLIP ViT-B/16 vision encoder
• Parameters: 86M
• Output: 512-dimensional L2-normalized embeddings (768d internal, projected to 512d via visual_projection)
• Input: 224x224 RGB image with CLIP normalization
• Size: ~329 MB
• Source: openai/clip-vit-base-patch16

Usage

# Embed a single image
crispembed -m clip-vit-base-patch16 --image photo.jpg

# Batch processing
crispembed -m clip-vit-base-patch16 --image-dir ./photos/ --output embeddings.bin

Cross-modal pairing

This model shares an embedding space with cstr/clip-text-base-GGUF. Use both to perform zero-shot image-text matching:

crispembed -m clip-vit-base-patch16 --image photo.jpg        # vision embedding
crispembed -m clip-text-base "a photo of a cat"               # text embedding

Cosine similarity between the two outputs measures image-text alignment.

Notes

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