The success of large language models (LLMs), like GPT-3 and ChatGPT, has led
to the development of numerous cost-effective and accessible alternatives that
are created by fine-tuning open-access LLMs with task-specific data (e.g.,
ChatDoctor) or instruction data (e.g., Alpaca). Among the various fine-tuning
methods, adapter-based parameter-efficient fine-tuning (PEFT) is undoubtedly
one of the most attractive topics, as it only requires fine-tuning a few
external parameters instead of the entire LLMs while achieving comparable or
even better performance. To enable further research on PEFT methods of LLMs,
this paper presents LLM-Adapters, an easy-to-use framework that integrates
various adapters into LLMs and can execute these adapter-based PEFT methods of
LLMs for different tasks. The framework includes state-of-the-art open-access
LLMs such as LLaMA, BLOOM, OPT, and GPT-J, as well as widely used adapters such
as Series adapter, Parallel adapter, and LoRA. The framework is designed to be
research-friendly, efficient, modular, and extendable, allowing the integration
of new adapters and the evaluation of them with new and larger-scale LLMs.
Furthermore, to evaluate the effectiveness of adapters in LLMs-Adapters, we
conduct experiments on six math reasoning datasets. The results demonstrate
that using adapter-based PEFT in smaller-scale LLMs (7B) with few extra
trainable parameters yields comparable, and in some cases superior, performance
to that of powerful LLMs (175B) in zero-shot inference on simple math reasoning
datasets. Overall, we provide a promising framework for fine-tuning large LLMs
on downstream tasks. We believe the proposed LLMs-Adapters will advance
adapter-based PEFT research, facilitate the deployment of research pipelines,
and enable practical applications to real-world systems.

本文提出 LLMs-Adapters 框架，利用少量可调参数对小型 LLMs 进行 fine-tuning，实现对各种任务的支持；在六种数学推理数据集上的实验表明，将 adapter-based PEFT 应用于小型 LLMs（7B）可以取得与强大的 LLMs（175B）相似甚至更优秀的性能，旨在推进 adapter-based PEFT 的研究，为 LM 大规模的 fine-tuning 提供了有价值的工具和框架。