This paper presents novel systems and methodologies for the development of efficient large language models (LLMs). It explores the trade-offs between model size, performance, and computational resources, with the aim of maximizing the efficiency of these AI systems. The research explores novel methods that allow different parts of the model to share parameters, reducing the total number of unique parameters required. This approach ensures that the model remains compact without sacrificing its ability to learn and represent complex language structures. This study provides valuable insights and tools for creating more efficient and effective LLMs, contributing to a more sustainable and accessible future for AI language modeling.

翻译：本文提出了开发高效大语言模型（LLMs）的新型系统和方法论。研究探讨了模型规模、性能与计算资源之间的权衡，旨在最大化这些人工智能系统的效率。本研究探索了允许模型不同部分共享参数的新方法，从而减少所需唯一参数的总数。该方法确保模型在保持紧凑的同时，不牺牲其学习和表示复杂语言结构的能力。本研究为创建更高效、更有效的大语言模型提供了宝贵的见解和工具，为AI语言建模更可持续、更易普及的未来做出贡献。