We present LongLoRA, an efficient fine-tuning approach that extends the
context sizes of pre-trained large language models (LLMs), with limited
computation cost. Typically, training LLMs with long context sizes is
computationally expensive, requiring extensive training hours and GPU
resources. For example, training on the context length of 8192 needs 16x
computational costs in self-attention layers as that of 2048. In this paper, we
speed up the context extension of LLMs in two aspects. On the one hand,
although dense global attention is needed during inference, fine-tuning the
model can be effectively and efficiently done by sparse local attention. The
proposed shift short attention effectively enables context extension, leading
to non-trivial computation saving with similar performance to fine-tuning with
vanilla attention. Particularly, it can be implemented with only two lines of
code in training, while being optional in inference. On the other hand, we
revisit the parameter-efficient fine-tuning regime for context expansion.
Notably, we find that LoRA for context extension works well under the premise
of trainable embedding and normalization. LongLoRA demonstrates strong
empirical results on various tasks on LLaMA2 models from 7B/13B to 70B.
LongLoRA adopts LLaMA2 7B from 4k context to 100k, or LLaMA2 70B to 32k on a
single 8x A100 machine. LongLoRA extends models' context while retaining their
original architectures, and is compatible with most existing techniques, like
FlashAttention-2. In addition, to make LongLoRA practical, we collect a
dataset, LongQA, for supervised fine-tuning. It contains more than 3k long
context question-answer pairs.

LongLoRA 是一种高效的微调方法，通过有限的计算成本扩展了预训练大型语言模型（LLMs）的上下文大小。