"Recent advancements in Large Language Models (LLMs) have markedly improved SQL generation. Nevertheless, existing approaches typically rely on single-model designs, limiting their capacity to effectively handle complex user queries. In addition, current methods often face difficulties in selecting the optimal SQL from multiple candidates. To mitigate these limitations,this study presents DSMR-SQL, a two-stage framework consisting of: (1) Dual-Strategy SQLGeneration: DSMR-SQL aims to produce a broader spectrum of SQL queries by using multiple models with two strategies: Supervised Fine-Tuning and In-Context Learning; (2) Multi-RoleSQL Selection: DSMR-SQL seeks to identify the SQL most aligning with user intent by introducing a collaborative framework involving three roles (i.e., Proposer, Critic, Summarizer).Extensive experiments on various datasets substantiate the efficacy of DSMR-SQL in enhancing SQL generation."