Parameter-efficient fine-tuning aims to achieve comparable performances of fine-tuning with much fewer trainable parameters. Recently, various tuning strategies (e.g., Adapters, Prefix Tuning, BitFit, and LoRA) have been proposed. However, their designs are hand-crafted separately, and it remains unclear whether certain design patterns exist for parameter-efficient fine-tuning. Thus, we present a parameter-efficient fine-tuning design paradigm and discover design patterns that are applicable to different experimental settings. Instead of focusing on designing another individual tuning strategy, we introduce parameter-efficient fine-tuning design spaces that parameterize tuning structures and tuning strategies. Specifically, any design space is characterized by four components: layer grouping, trainable parameter allocation, tunable groups, and strategy assignment. Our comprehensive empirical study leads to the discovery of design patterns: (i) grouping layers in a spindle pattern, (ii) uniformly allocating the number of trainable parameters to layers, (ii) tuning all the groups, and (iv) tuning different groups with proper strategies. Our discovered design patterns result in new parameter-efficient fine-tuning methods. Experiments show that these methods consistently outperform investigated parameter-efficient fine-tuning strategies across different backbone models and different tasks in natural language processing.