Model-Preserving Adaptive Rounding with YAQA
Together AI
Researchers introduced YAQA, a weight-only quantization method for large language models that directly minimizes KL divergence to the original model by using a Kronecker-factored Hessian approximation instead of layer-wise activation error minimization. YAQA reduced KL divergence by more than 30% across multiple models and quantizers compared to existing methods like LDLQ and GPTQ. The approach enables better model compression without requiring retraining and works with any hardware quantizer or memory-bound quantization method.
