given this abstract : 
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 finetuning 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 smallerscale LLMs (7B) with few extra trainable parameters yields comparable, and in some cases
superior, performance to that of powerful LLMs (175B) in zero-shot inferen