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Maxime Labonne on Model Merging, AI Trends, and Beyond

How AI Is Built

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Mastering Model Merging in AI

This chapter explores the concept of model merging in artificial intelligence, detailing its motivations and the complexities involved in the process. It examines the techniques used for merging models, such as averaging parameters and combining distinct capabilities, while also addressing the challenges faced, like issues with tokenizers. The discussion highlights the growing acceptance of model merging in the industry and its potential for enhancing performance through innovative approaches.

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