A new PyTorch-native library, torchtune, has been introduced to simplify the post-training process of large language models (LLMs). This library aims to provide an efficient and streamlined approach to fine-tuning, experimentation, and deployment of LLMs, which typically require complex multistage training pipelines to achieve strong performance. By leveraging torchtune, developers can adapt open-weight models for specific tasks, enhancing their overall effectiveness. The library's native integration with PyTorch enables seamless workflows, facilitating the deployment of LLMs in various applications. The development of torchtune is significant as it addresses the need for efficient post-training processes, which is crucial for optimizing LLMs1. This matters to practitioners as it enables them to focus on high-level tasks, such as model customization and deployment, rather than getting bogged down in low-level implementation details.