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Large Language Models (LLMs) are versatile but fine-tuning them for specific tasks unlocks their full potential. This article explores the art of fine-tuning LLMs, its process, and applications in chatbots, translation, and sentiment analysis. It also addresses ethical considerations and the challenges of bias, data scarcity, overfitting, and computational requirements in fine-tuning.

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