G$^2$PTQ: Improving LLM Post-Training Quantization with Generalized Gradient Compensation
G$^2$PTQ is presented, a unified PTQ framework with Generalized Gradient Compensation that integrates both first- and second-order information under a globally supervised, block-wise optimization objective and enables better alignment with the full-precision model, outperforming state-of-the-art baselines.
Rui-Kang Liu, Hao-Li Bai, Yuxuan Sun et al.
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