Parameter-Efficient Fine-Tuning for Generative AI: Implementations, Best Practices, and Trade-Offs

Large language models (LLMs) have revolutionized natural language processing (NLP), yet full fine-tuning remains computationally expensive. Parameter-efficient fine-tuning (PEFT) techniques offer a solution by modifying only a small number of parameters. This paper provides a comparative analysis of key PEFT strategies, including Adapters, Low-Rank Adaptation (LoRA), Prompt Tuning, and Prefix Tuning, discussing their best practices, trade-offs, and empirical evaluations. We present a real-world implementation demonstrating the efficacy of these methods on a text classification task.