Feather-SQL: A Lightweight NL2SQL Framework with Dual-Model Collaboration Paradigm for Small Language Models

Natural Language to SQL (NL2SQL) has seen significant advancements with large language models (LLMs). However, these models often depend on closed-source systems and high computational resources, posing challenges in data privacy and deployment. To address these issues, we introduce Feather-SQL, a new lightweight framework tailored for SLMs. Feather-SQL improves SQL executability and accuracy through 1) schema pruning and linking, 2) multi-path and multi-candidate generation. Additionally, we introduce the 1+1 Model Collaboration Paradigm, which pairs a strong general-purpose chat model with a fine-tuned SQL specialist, combining strong analytical reasoning with high-precision SQL generation. Experimental results on BIRD demonstrate that Feather-SQL improves NL2SQL performance on SLMs, with around 10% boost for models without fine-tuning. The proposed paradigm raises the accuracy ceiling of SLMs to 54.76%, highlighting its effectiveness. Natural Language to SQL (NL2SQL) is the task of converting natural language questions into corresponding SQL queries, allowing users to retrieve structured data from databases without requiring proficiency in SQL language. In recent years, the field has seen significant advancements with the emergence of large language models (LLMs) such as GPT-4 (OpenAI, 2024), enabling frameworks like CHASE-SQL (Pourreza et al., 2024) and XiYan-SQL (Gao et al., 2025) to achieve state-of-the-art (SOTA) performance. However, two limitations hinder their practical adoption.