---
license: bigscience-openrail-m
language:
- en
base_model:
- Qwen/Qwen2.5-Coder-3B-Instruct
pipeline_tag: translation
---

### Performance on the BIRD Development Set

We further evaluate **DatA-SQL-3B** on the **BIRD** development set using different self-consistency voting sizes.  
Under **Vote@8**, our model attains an **execution accuracy (EX) of 61.05 %**.
When the voting size increases to **Vote@32**, the EX further improves to **62.58 %**.
These results confirm that larger voting ensembles enhance semantic robustness and execution stability while maintaining nearly the same inference cost due to our lightweight multi-agent design.  
Overall, **DatA-SQL** achieves competitive or superior accuracy compared with GPT-based pipelines at only a fraction of their computational expense.