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- W4386793352 abstract "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." @default.
- W4386793352 created "2023-09-16" @default.
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- W4386793352 date "2023-09-12" @default.
- W4386793352 modified "2023-09-30" @default.
- W4386793352 title "Do Generative Large Language Models need billions of parameters?" @default.
- W4386793352 doi "https://doi.org/10.48550/arxiv.2309.06589" @default.
- W4386793352 hasPublicationYear "2023" @default.
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