TinyCLIP is a compact vision–language model that compresses CLIP through knowledge distillation, enabling efficient image–text representation learning with significantly lower compute and memory requirements.

Original paper: TinyCLIP: CLIP Distillation via Affinity Mimicking and Weight Inheritance, Wu et al., 2023

This model uses the TinyCLIP variant, optimized for efficient image–text embedding generation while preserving strong zero-shot classification and retrieval performance. It is well suited for applications such as image retrieval, zero-shot classification, multimodal search, and edge vision-language deployments.

Model Configuration: