This paper provides a comprehensive analysis of Parameter Efficient Fine-Tuning (PEFT) techniques across various applications. PEFT aims to reduce computational and memory costs while maintaining model performance by selectively updating a subset of parameters. Traditional fine-tuning methods, which adjust all parameters, are computationally expensive and memory-intensive, prompting the development of PEFT. The paper reviews PEFT approaches, comparing their effectiveness in reducing computational load, speeding up training, and lowering memory usage. It highlights applications in text generation, medical imaging, protein modeling, speech synthesis, and code review. The paper discusses specific PEFT methods such as LoReFT, which achieves state-of-the-art performance in commonsense reasoning and arithmetic tasks. It also explores video-text generation using AGAdapter, which achieves state-of-the-art results on benchmarks like MSR-VTT and ActivityNet. In medical imaging, PEFT methods like Adapter, BitFit, and LoRA show significant performance gains, especially in data-scarce scenarios. For protein modeling, PEFT methods achieve comparable or superior performance to traditional fine-tuning with fewer parameters. In code review, LLaMA-Reviewer, using PEFT, achieves high accuracy in tasks like review necessity prediction and code refinement. In speech synthesis, LoRA outperforms other PEFT methods in emotion recognition tasks. The paper also discusses challenges in PEFT, including balancing efficiency and performance, data scarcity, overfitting, and capacity constraints of incremental modules. It proposes future research directions, such as task-agnostic PEFT techniques, privacy-preserving PEFT for sensitive data, and improving PEFT in limited labeled data scenarios. The study concludes that PEFT is a promising approach for efficient and effective model fine-tuning across diverse applications.This paper provides a comprehensive analysis of Parameter Efficient Fine-Tuning (PEFT) techniques across various applications. PEFT aims to reduce computational and memory costs while maintaining model performance by selectively updating a subset of parameters. Traditional fine-tuning methods, which adjust all parameters, are computationally expensive and memory-intensive, prompting the development of PEFT. The paper reviews PEFT approaches, comparing their effectiveness in reducing computational load, speeding up training, and lowering memory usage. It highlights applications in text generation, medical imaging, protein modeling, speech synthesis, and code review. The paper discusses specific PEFT methods such as LoReFT, which achieves state-of-the-art performance in commonsense reasoning and arithmetic tasks. It also explores video-text generation using AGAdapter, which achieves state-of-the-art results on benchmarks like MSR-VTT and ActivityNet. In medical imaging, PEFT methods like Adapter, BitFit, and LoRA show significant performance gains, especially in data-scarce scenarios. For protein modeling, PEFT methods achieve comparable or superior performance to traditional fine-tuning with fewer parameters. In code review, LLaMA-Reviewer, using PEFT, achieves high accuracy in tasks like review necessity prediction and code refinement. In speech synthesis, LoRA outperforms other PEFT methods in emotion recognition tasks. The paper also discusses challenges in PEFT, including balancing efficiency and performance, data scarcity, overfitting, and capacity constraints of incremental modules. It proposes future research directions, such as task-agnostic PEFT techniques, privacy-preserving PEFT for sensitive data, and improving PEFT in limited labeled data scenarios. The study concludes that PEFT is a promising approach for efficient and effective model fine-tuning across diverse applications.