LLM fine-tuning is the process of adapting pre-trained large language models to specific tasks, domains, or behaviors by continuing training on custom datasets. Born from the need to customize foundation models without the astronomical cost of training from scratch, fine-tuning has evolved into a sophisticated discipline encompassing parameter-efficient methods (PEFT), alignment techniques (RLHF, DPO), and advanced optimization strategies. The key insight is that strategic parameter updates unlock specialized performance—a 7B model fine-tuned with LoRA on 1,000 examples can outperform a generic 70B model on domain-specific tasks, making fine-tuning both an art of data curation and a science of efficient training.

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