Large Language Models (LLMs) have showcased remarkable impacts across a wide
spectrum of natural language processing tasks. Fine-tuning these pre-trained
models on downstream datasets provides further significant performance gains,
but this process has been challenging due to its extraordinary resource
requirements. To this end, existing efforts focus on parameter-efficient
fine-tuning, which, unfortunately, fail to capitalize on the powerful potential
of full-parameter fine-tuning. In this work, we propose QFT, a novel Quantized
Full-parameter Tuning framework for LLMs that enables memory-efficient
fine-tuning without harming performance. Our framework incorporates two novel
ideas: (i) we adopt the efficient Lion optimizer, which only keeps track of the
momentum and has consistent update magnitudes for each parameter, an inherent
advantage for robust quantization; and (ii) we quantize all model states and
store them as integer values, and present a gradient flow and parameter update
scheme for the quantized weights. As a result, QFT reduces the model state
memory to 21% of the standard solution while achieving comparable performance,
e.g., tuning a LLaMA-7B model requires only <30GB of memory, satisfied by a
single A6000 GPU.

提出了一种名为 QFT 的新型量化全参数调优框架，可以实现内存高效调优而不损害性能。该框架采用高效的 Lion 优化器和整数值量化的模型状态存储方法，并提供了梯度流和参数更新方案。结果表明，QFT 将模型状态内存减少到标准解决方案的 21%，同时达到可比较的性能，例如，调优 LLaMA-7B 模型仅需 30GB 内存，一张 A6000 GPU 即可满足。

QFT：量子化的低资源 LLM 全参数调整

QFT: Quantized Full-parameter Tuning of LLMs with Affordable Resources

The post-training quantization (PTQ) challenge of bringing quantized neural
net accuracy close to original has drawn much attention driven by industry
demand. Many of the methods emphasize optimization of a specific
degree-of-freedom (DoF), such as quantization step size, preconditioning
factors, bias fixing, often chained to others in multi-step solutions. Here we
rethink quantized network parameterization in HW-aware fashion, towards a
unified analysis of all quantization DoF, permitting for the first time their
joint end-to-end finetuning. Our single-step simple and extendable method,
dubbed quantization-aware finetuning (QFT), achieves 4-bit weight quantization
results on-par with SoTA within PTQ constraints of speed and resource.

提出了一种硬件意识的量化网络参数化方法 —— 量化感知微调（QFT），可以通过联合端对端微调实现一步到位的量化，获得与最优结果相当的 4 位权重量化结果。