ZhSQL-R1-1.5B is a bilingual Text-to-SQL model based on Qwen2.5-Coder-1.5B, trained using supervised fine-tuning (SFT) followed by execution-reward GRPO. The model was fine-tuned for two epochs on 7,127 examples derived from the BIRD benchmark, with approximately 48% Chinese prompts. Key settings: LoRA rank 32, maximum sequence length 3,072, effective batch size 16, and learning rate 2e-4. Loss was applied only to the SQL response. The SFT model generated eight SQL candidates per question. Examples with partial success (0 < passrate < 1) were retained, producing 3,692 learnable examples. GRPO used binary execution-equivalence rewards for 300 steps, with eight generations per prompt, LoRA…
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