Full Parameter Fine-tuning for Large Language Models with Limited
  Resources
Authors: name: Kai Lv, name: Yuqing Yang, name: Tengxiao Liu, name: Qinghui Gao, name: Qipeng Guo, name: Xipeng Qiu

Large Language Models (LLMs) have revolutionized Natural Language Processing
(NLP) but demand massive GPU resources for training. Lowering the threshold for
LLMs training would encourage greater participation from researchers,
benefiting both academia and society. While existing approaches have focused on
parameter-efficient fine-tuning, which tunes or adds a small number of
parameters, few have addressed the challenge of tuning the full parameters of
LLMs with limited resources. In this work, we propose a new optimizer,
LOw-Memory Optimization (LOMO), which fuses the gradient computation and the
parameter update in one step to reduce memory usage. By integrating LOMO with
existing memory saving techniques, we reduce memory usage to 10.8% compared to
the standard approach (DeepSpeed solution). Consequently, our approach enables
the full parameter fine-tuning of a 65B model on a single machine with 8 RTX
3090, each with 24GB memory.