FINER-SQL-0.5B-Spider

A small but capable 0.5 B-parameter Text-to-SQL model fine-tuned from griffith-bigdata/Qwen-2.5-Coder-0.5B-SQL-Writer with GRPO + the FINER-SQL dense rewards (Memory + Atomic).

✅ 75.0% Execution Accuracy on Spider Dev (n=30, value-aware voting). Runs on a 4-8 GB GPU.

📄 See other models: https://huggingface.co/collections/griffith-bigdata/finer-sql 📄 GitHub: https://github.com/thanhdath/finer-sql/tree/main

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FINER-SQL Model Family — Comparison Across All Sizes

Model	Params	BIRD Dev (n=30, vav)	Spider Dev (n=30, vav, +agg_hint)
FINER-SQL-3B-BIRD	3 B	67.54% ✅	83.8%
FINER-SQL-3B-Spider	3 B	63.04%	85.10% ✅
FINER-SQL-0.5B-BIRD	0.5 B	50.85% ✅	68.6%
FINER-SQL-0.5B-Spider (this model)	0.5 B	TBD	75.0% ✅

The 0.5 B Spider model is 6.4 pp better than the 0.5 B BIRD model on Spider Dev — confirming dataset-specific specialisation matters even at small scales.

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Inference

Quick start (vLLM)

from vllm import LLM, SamplingParams

llm = LLM(
    model="griffith-bigdata/FINER-SQL-0.5B-Spider",
    dtype="bfloat16",
    max_model_len=4096,
    gpu_memory_utilization=0.7,
)

system_prompt = """You are a meticulous SQL expert. Generate a single, correct SQL query for the user question and the provided database schema.
Follow this exact response format:

Rules:
- Output exactly one SQL statement.
- The SQL must be executable on SQLite.
- Do not include any explanatory text.
- Output one SQL statement only. Do not include any extra text, tags, or code fences."""

sampling = SamplingParams(n=30, temperature=1.0, max_tokens=2048)
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": f"Database Schema:\n{schema}\n\nQuestion: {question}"},
]
output = llm.chat(messages, sampling)
candidate_sqls = [c.text.split("</think>")[-1].strip() for c in output[0].outputs]
# Apply majority voting (vav) — see GitHub repo

Recommended evaluation pipeline

1. Generate n=30 candidates with temperature=1.0
2. Execute each candidate; group results
3. Pick from the largest non-empty success group (value-aware voting, "vav")
4. Score with the official Spider evaluator (test_suite_sql_eval)

This pipeline gives 75.0% Spider Dev EX (75.44% MV).

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Detailed Spider Dev results (n=30, vav)

Hardness	Count	Execution Accuracy
Easy	248	91.9%
Medium	446	82.5%
Hard	174	62.6%
Extra Hard	166	42.8%
All	1034	75.0%

Recall@30: 85.11% (any-correct rate among 30 candidates).

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Training

Parameter	Value
Base model	griffith-bigdata/Qwen-2.5-Coder-0.5B-SQL-Writer
Algorithm	GRPO
Train data	Spider train (8,659 samples)
Total steps	2000 (this checkpoint = 2000)
Learning rate	8e-6
Num generations per prompt	32
Gradient accumulation	32
Max completion length	2048
Max prompt length	1500
Temperature (rollout)	1.0
Selection during eval	vav (value-aware voting)
Rewards	Execution + Atomic + Memory + Format

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License

Inherits the base model's license (Apache 2.0).

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Citation

@article{finer-sql-2026,
  title  = {FINER-SQL: Fine-grained reasoning rewards for small Text-to-SQL models},
  author = {Thanh Dat and others},
  year   = {2026},
}