<ul data-eligibleForWebStory="true"><li>The article delves into fine-tuning large language models (LLMs) on Amazon SageMaker AI.</li><li>It covers pre-training, continued pre-training, fine-tuning methods, alignment strategies, optimization techniques.</li><li>Discusses Parameter-Efficient Fine-Tuning (PEFT) methods, Reinforcement Learning from Human Feedback, Supervised Fine-Tuning.</li><li>Additionally explores mixed precision training, gradient accumulation, and knowledge distillation for efficient model adaptation.</li><li>Addresses practical implementation and considerations of cost, performance, and operational efficiency in AI.</li></ul>

Advanced fine-tuning methods on Amazon SageMaker AI

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