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The End of Finetuning — with Jeremy Howard of Fast.ai

Latent Space: The AI Engineer Podcast

CHAPTER

The Fine-Tuning Frontier in Machine Learning

This chapter delves into the development and significance of fine-tuning in machine learning, contrasting it with traditional approaches like retrieval augmented generation. The conversation highlights the evolution of frameworks, such as JAX and Mojo, and discusses the implications of fine-tuning for small models and enterprise applications. It emphasizes the importance of accessible tools for new developers and the uncertainties facing the community regarding future advancements in AI technology.

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