Generative Language Models (GLMs) have shown impressive performance in tasks
such as text generation, understanding, and reasoning. However, the large model
size poses challenges for practical deployment. To solve this problem,
Quantization-Aware Training (QAT) has become increasingly popular. However,
current QAT methods for generative models have resulted in a noticeable loss of
accuracy. To counteract this issue, we propose a novel knowledge distillation
method specifically designed for GLMs. Our method, called token-scaled logit
distillation, prevents overfitting and provides superior learning from the
teacher model and ground truth. This research marks the first evaluation of
ternary weight quantization-aware training of large-scale GLMs with less than
1.0 degradation in perplexity and no loss of accuracy in a reasoning task.

通过提出一种称为令牌缩放逻辑蒸馏的知识蒸馏方法，我们对大规模生成式语言模型进行了三值权重量化训练的首次评估，其中困惑度下降不到 1.0，推理任务并无准确性损失。