A mid-sized multimodal model that handles both text and image inputs, quantized to int4 precision using AutoRound with error correction to reduce memory footprint while preserving output quality. The quantization approach aims to minimize accuracy loss compared to naive int4 methods, making it more practical for deployment on constrained hardware. Trade-offs typical of aggressive quantization — subtle degradation on complex reasoning tasks — may still apply.