LLMB ox: A Comprehensive Library for Large Language Models (2024)

Abstract: To facilitate the research on large language models (LLMs), this paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of LLMs. This library is featured with three main merits: (1) a unified data interface that supports the flexible implementation of various training strategies, (2) a comprehensive evaluation that covers extensive tasks, datasets, and models, and (3) more practical consideration, especially on user-friendliness and efficiency. With our library, users can easily reproduce existing methods, train new models, and conduct comprehensive performance comparisons. To rigorously test LLMBox, we conduct extensive experiments in a diverse coverage of evaluation settings, and experimental results demonstrate the effectiveness and efficiency of our library in supporting various implementations related to LLMs. The detailed introduction and usage guidance can be found at https://github.com/RUCAIBox/LLMBox .

## introduction recent years have witnessed the rapid progress of large language models (llms) @xcite . in the research community, great efforts have been devoted to the release of well-trained llms, the design of special tuning and inference methods, and the conduct of systematic capacity evaluation. however, the reproducibility and fair comparison of existing studies should still be emphasized, since they are mostly developed in different ways or frameworks. without the standardized and unified implementation, it would take substantial efforts to reproduce or improve upon existing research work. considering the above issue, in this paper, we present a comprehensive library, called llmbox, for easing the development, use, and evaluation of llms. in particular, our library focuses on building a comprehensive and unified framework (including training, inference, and evaluation) for better supporting llm-based research and applications. although there are already several opensource libraries for llms @xcite @xcite , they typically focus on a certain or some stage(s) of llms (either pre-training or fine-tuning) or conduct the training pipeline of llms in a separate way. moreover, they can seldom support comprehensive and unified evaluation of various llms. in order to better facilitate research on llms, llmbox introduces a series of new features for the library design, which can be summarized into three major aspects below: an additional feature is that llmbox is closely aligned with our prior survey paper on llms @xcite . this is particularly useful for beginners, enabling the learning of basic knowledge and practice of llms through integrating the survey paper and the associated library. in what follows, we will first introduce the training framework of our library in section 2, then detail the utilization and evaluation parts in section 3, and showcase how to use our library in section 4. after that, we will conduct the experiments to verify the reliability of our llmbox in section 5, and conclude the paper in section 6. ## training the training process is a crucial step for the development of llms. however, it typically needs massive detailed designs considering both efficiency and effectiveness, and also often faces intractable problems when adapting into new domains or meeting special needs. to facilitate easy training of llms, we integrate various training methods and resources in our library, to unify and simplify their usage. besides, we provide suggestions for gpu usage tailored to different training requirements. ## utilization and evaluation after training, we can develop suitable prompting strategies to use llms and assess their effectiveness. users can reuse existing models, apis or the models trained by llmbox. the framework of our utilization pipeline is depicted in figure 1 . ## library usage in this section, we present the application of our library across four distinct research scenarios, illustrated through example code snippets. ## experiment in the section, we conduct extensive experiments to verify the effectiveness and efficiency. ## conclusion this paper presented llmbox, a comprehensive library for conducting research on training, utilizing, and evaluating large language models. for training, we designed a unified data interface to support the implementation of various training strategies. for utilization and evaluation, we implemented typical approaches to use llms (including quantization, icl, and cot prompting), covered 18 tasks and 56 datasets, and included a number of popular opensourced llms and closed-source apis. we further conducted extensive experiments to verify the effectiveness and efficiency of llmbox. our library provides a unified framework to compare, reproduce, and develop llms and supporting methods for academic purposes, which would be of important value to promote the research on llms.