  Emergent Large Language Models (LLMs) use their extraordinary performance and
powerful deduction capacity to discern from traditional language models.
However, the expenses of computational resources and storage for these LLMs are
stunning, quantization then arises as a trending conversation. To address
accuracy decay caused by quantization, two streams of works in post-training
quantization methods stand out. One uses other weights to compensate existing
quantization error, while the other transfers the quantization difficulty to
other parts in the model. Combining both merits, we introduce Learnable
Singular value Increment (LSI) as an advanced solution. LSI uses Singular Value
Decomposition to extract singular values of the weights and make them learnable
to help weights compensate each other conditioned on activation. Incorporating
LSI with existing techniques, we achieve state-of-the-art performance in
diverse quantization settings, no matter in weight-only, weight-activation or
extremely low bit scenarios. By unleashing the potential of LSI, efficient
finetuning on quantized model is no longer a prohibitive problem.
