Large language models(LLMs) exhibit excellent performance across a variety of
tasks, but they come with significant computational and storage costs.
Quantizing these models is an effective way to alleviate this issue. However,
existing methods struggle to strike a balance between model accuracy and
hardware efficiency. This is where we introduce AWEQ, a post-training method
that requires no additional training overhead. AWEQ excels in both
ultra-low-bit quantization and 8-bit weight and activation (W8A8) quantization.
There is an observation that weight quantization is less challenging than
activation quantization. AWEQ transfers the difficulty of activation
quantization to weights using channel equalization, achieving a balance between
the quantization difficulties of both, and thereby maximizing performance. We
have further refined the equalization method to mitigate quantization bias
error, ensuring the robustness of the model. Extensive experiments on popular
models such as LLaMA and OPT demonstrate that AWEQ outperforms all existing
post-training quantization methods for large models.

大型语言模型具有卓越的性能，但是它们带来了重大的计算和存储成本。用 8 位权重和激活（W8A8）量化和超低比特量化进行权重和激活量化的 AWEQ 方法使两者的量化难度达到平衡，并实现了最大化的性能。