  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 achieves enhanced accuracy in tasks like
common-sense QA and arithmetic reasoning as well as natural language
understanding. Our code is available at https://github.com/aiha-lab/TSLD.
