SQL-GRPO 1.5B

A LoRA adapter for Qwen2.5-1.5B-Instruct, trained with GRPO (Group Relative Policy Optimization) to turn natural-language questions into SQLite queries. The reward comes from executing the generated SQL against the real database: no human-labeled feedback, the database is the judge.

🚀 Live demo · 💻 Code

Results

Spider dev set (1,034 questions on databases not seen during training), execution accuracy:

Model	Accuracy	Valid SQL	Correct format
Qwen2.5-1.5B-Instruct (untrained)	40.72%	58.41%	76.89%
This model (GRPO)	57.74%	82.40%	100.00%
gpt-4.1-mini (prompted, for reference)	74.37%	98.74%	100.00%

GRPO closed about half the gap to gpt-4.1-mini, with gains on every question type: aggregation +18.3 points, GROUP BY +15.5, nested queries +11.9, JOINs +10.0.

How to use

import re
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct", torch_dtype="auto")
model = PeftModel.from_pretrained(base, "bimal2004/sql-grpo-1.5b")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")

schema = """CREATE TABLE singer (
  Singer_ID int, Name text, Country text, Age int,
  PRIMARY KEY (Singer_ID)
)"""
question = "How many singers are from France?"

messages = [
    {"role": "system", "content": (
        "You are a SQLite expert. Given a database schema and a question, "
        "write one SQLite query that answers it. "
        "Put only the query inside <sql></sql> tags.")},
    {"role": "user", "content": f"Schema:\n{schema}\n\nQuestion: {question}"},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
answer = tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)

sql = re.search(r"<sql>(.*?)</sql>", answer, re.DOTALL).group(1).strip()
print(sql)

Prompt format matters: the model was trained with exactly this system prompt, with the schema given as CREATE TABLE statements, and answers inside <sql></sql> tags.

Training details

Method: GRPO via Hugging Face TRL GRPOTrainer + Unsloth, LoRA rank 32 (about 3% of parameters trained)
Data: Spider train split (6,289 questions after filtering very long schemas)
Reward: +1.0 if the query returns the same rows as the gold SQL, +0.2 if it runs without error, +0.1 for correct <sql> formatting, −0.1 for overly long output. Queries run on a read-only connection with a 2-second timeout.
Setup: 400 steps, 8 sampled answers per question, learning rate 5e-6, on a single free Kaggle T4 GPU

Limitations

Trained and evaluated only on Spider (SQLite); performance on other SQL dialects or very large real-world schemas is untested.
The most common remaining error is schema linking: referencing columns that don't exist (about a third of failures). JOINs and nested queries remain the hardest cases (~40% accuracy).
Some correct answers on borderline questions can flip under different numerical precision or batching. Evaluate on many questions, not single examples.
Always review generated SQL before running it on important data.