anonymous-2321/Think2SQL-4B
anonymous-2321/Think2SQL-4B is a 4 billion parameter reasoning model, fine-tuned from Qwen/Qwen3-4B, specifically designed for Text-to-SQL tasks. It leverages a systematic study on injecting reasoning capabilities through Reinforcement Learning with Verifiable Rewards (RLVR). This model excels at generating valid SQL queries from natural language questions, evidence, and database schemas, making it highly effective for data science applications requiring SQL generation.
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Think2SQL-4B: Text-to-SQL Reasoning Model
Think2SQL-4B is a 4 billion parameter model, fine-tuned from Qwen/Qwen3-4B, engineered for advanced Text-to-SQL reasoning. It is based on the Think2SQL framework, which systematically injects reasoning capabilities into Text-to-SQL tasks using Reinforcement Learning with Verifiable Rewards (RLVR). The model was trained on the BIRD dataset with thinking disabled, utilizing the TRL library.
Key Capabilities
- Text-to-SQL Generation: Generates accurate SQL queries from natural language questions, provided evidence, and database schemas.
- Reasoning Integration: Incorporates a structured reasoning process, allowing it to first think through the problem before providing the SQL answer.
- Optimized for Performance: Achieves reasoning capabilities competitive with state-of-the-art models while operating under computational constraints.
- Execution-Guided Rewards: Utilizes novel execution-guided dense reward functions, outperforming simpler binary signals in training.
Good For
- Data Science Experts: Ideal for data scientists and developers needing to convert complex natural language queries into precise SQL.
- Database Interaction: Automating SQL query generation for various database engines, demonstrated with SQLite in examples.
- Research in Text-to-SQL: Provides a comprehensive analysis for optimizing Text-to-SQL reasoning, including insights into reward density, advantage scaling, and model capacity.