CBQ (Cross-Block Quantization) introduces a novel approach to compress Large Language Models while maintaining performance, even at low-bit settings.

• Addresses limitations of current post-training quantization methods by considering dependencies between blocks, not just within them
• Employs homologous reconstruction to preserve model capabilities during compression
• Achieves superior performance compared to existing quantization techniques for LLMs
• Enables more efficient deployment of large models on resource-constrained devices

This engineering advancement matters because it dramatically reduces computational and memory requirements for LLMs without sacrificing quality, making advanced AI more accessible and affordable across platforms.

CBQ: Cross-Block Quantization for Large Language Models