XGenerationLab/XiYanSQL-QwenCoder-7B-2502
The XiYanSQL-QwenCoder-7B-2502 is a 7.6 billion parameter model developed by XGenerationLab, specifically designed for text-to-SQL tasks. This model excels at generating SQL queries from natural language, supporting multiple dialects including SQLite, PostgreSQL, and MySQL. It demonstrates strong performance on benchmarks like BIRD and Spider, making it suitable for direct use in SQL generation or as a base for fine-tuning specialized SQL models.
Loading preview...
XiYanSQL-QwenCoder-7B-2502: Specialized Text-to-SQL Model
The XiYanSQL-QwenCoder-7B-2502 is part of the XiYanSQL-QwenCoder series by XGenerationLab, focusing on advancing large language models for text-to-SQL generation. This 7.6 billion parameter model is optimized for converting natural language questions into SQL queries across various database dialects.
Key Capabilities
- High Performance: The series, including the 32B variant, has achieved state-of-the-art results on the BIRD TEST set with a 69.03% EX score, and the 7B model shows competitive performance on BIRD and Spider benchmarks.
- Multi-Dialect Support: It supports mainstream SQL dialects such as SQLite, PostgreSQL, and MySQL, offering flexibility for diverse database environments.
- Flexible Usage: Can be directly applied to text-to-SQL tasks or serve as a robust foundation for further fine-tuning to specific SQL generation needs.
Performance Highlights
On the BIRD Dev@M-Schema benchmark, the XiYanSQL-QwenCoder-7B achieved 59.78%, and on Spider Test@M-Schema, it scored 84.86%. These figures position it strongly against other large models in its class. The model is designed to work effectively with both M-Schema and DDL schema formats.
Good For
- Developers requiring accurate and efficient SQL generation from natural language.
- Projects involving database interaction where automated query creation is beneficial.
- As a base model for fine-tuning on custom text-to-SQL datasets.