Arcee's MergeKit is an open-source library designed to facilitate the merging of large language models (LLMs) without the need for additional training. The paper introduces MergeKit, which provides a comprehensive framework for merging models, enabling multitask learning and reducing the risk of catastrophic forgetting. The library supports various merging techniques, including linear interpolation, spherical interpolation, and permutation symmetry-based methods, and is compatible with different hardware configurations. MergeKit has been widely adopted by the open-source community, leading to the creation of some of the most powerful open-source model checkpoints, as assessed by the Open LLM Leaderboard. The library's design emphasizes user-friendliness, modularity, interoperability, scalability, and community engagement. It allows users to define complex merge operations through YAML configuration files, making it accessible to both novice and expert users. The paper also discusses the practical applications of model merging, including its effectiveness in enhancing performance across various domains, such as medical tasks. The success of merged models is demonstrated through examples like BioMistral and OpenPipe's Mistral 7B Fine-Tune Optimized. The paper highlights the importance of model merging in advancing the field of natural language processing by enabling the creation of versatile and robust models that can perform multiple tasks simultaneously. MergeKit's extensibility allows for the integration of new merging techniques, fostering innovation and collaboration within the open-source community. The library's popularity is reflected in its rapid growth on GitHub, with a significant number of stars and contributions. The paper concludes by emphasizing the transformative potential of model merging in enhancing the capabilities of LLMs while ensuring ethical considerations and responsible use of AI technologies.Arcee's MergeKit is an open-source library designed to facilitate the merging of large language models (LLMs) without the need for additional training. The paper introduces MergeKit, which provides a comprehensive framework for merging models, enabling multitask learning and reducing the risk of catastrophic forgetting. The library supports various merging techniques, including linear interpolation, spherical interpolation, and permutation symmetry-based methods, and is compatible with different hardware configurations. MergeKit has been widely adopted by the open-source community, leading to the creation of some of the most powerful open-source model checkpoints, as assessed by the Open LLM Leaderboard. The library's design emphasizes user-friendliness, modularity, interoperability, scalability, and community engagement. It allows users to define complex merge operations through YAML configuration files, making it accessible to both novice and expert users. The paper also discusses the practical applications of model merging, including its effectiveness in enhancing performance across various domains, such as medical tasks. The success of merged models is demonstrated through examples like BioMistral and OpenPipe's Mistral 7B Fine-Tune Optimized. The paper highlights the importance of model merging in advancing the field of natural language processing by enabling the creation of versatile and robust models that can perform multiple tasks simultaneously. MergeKit's extensibility allows for the integration of new merging techniques, fostering innovation and collaboration within the open-source community. The library's popularity is reflected in its rapid growth on GitHub, with a significant number of stars and contributions. The paper concludes by emphasizing the transformative potential of model merging in enhancing the capabilities of LLMs while ensuring ethical considerations and responsible use of AI technologies.