---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-Coder-1.5B-Instruct
pipeline_tag: translation
---

### Performance on the BIRD Development Set

We further evaluate **DatA-SQL-1.5B** on the **BIRD** development set using self-consistency voting.  
Under **Vote@8**, our model achieves an **execution accuracy (EX) of 55.86 %**, establishing a new state of the art among open-source Text-to-SQL systems at the 1.5 B scale.  
This result demonstrates that even compact models can acquire strong reasoning and SQL generation abilities when trained with our **reasoning-guided data augmentation**. 
The lightweight design ensures low training and inference costs while maintaining high execution robustness.  
Overall, **DatA-SQL-1.5B** achieves competitive accuracy compared with much larger GPT-based pipelines, highlighting the efficiency and practicality of our approach.