qwen_4b_sql

qwen_4b_sql is a Qwen3-4B-Base model finetuned for text-to-SQL generation with full SFT on a cleaned split of PipableAI/pip-txt-to-sql-spider-bird-dataset.

This repository tracks the stronger 4B checkpoint from our H20 single-GPU training runs. In our internal comparison, this checkpoint outperformed the corresponding Qwen3-1.7B-Base baseline on Spider execution accuracy.

Base Model

• Base model: Qwen/Qwen3-4B-Base
• Finetuning framework: LLaMA-Factory
• Training mode: Full SFT
• Task: schema + question -> SQL only

Training Data

• Primary dataset: PipableAI/pip-txt-to-sql-spider-bird-dataset
• We used a cleaned local split derived from that dataset for train/validation

Training Setup

• Hardware: single NVIDIA H20 96GB
• Precision: bf16
• Context length: 2048
• Per-device train batch size: 1
• Gradient accumulation steps: 8
• Effective batch size: 8
• Learning rate: 5e-6
• Scheduler: cosine
• Warmup steps: 300
• Epochs: 4.0
• Template: qwen3_nothink
• Best-checkpoint selection: load_best_model_at_end = true

Spider Benchmark

The following numbers are from Spider dev using the official evaluation tooling:

• Official match evaluation from test-suite-sql-eval
• Official Spider Test Suite execution evaluation

Main Results

Metric	Score
Spider official exact match	35.0%
Spider Test Suite execution accuracy	67.6%

Difficulty Breakdown

Difficulty	Exact Match	Test Suite Exec
Easy	64.9%	87.5%
Medium	37.4%	72.9%
Hard	16.1%	50.0%
Extra	3.6%	42.2%

Notes

• This model is stronger under execution-based Spider evaluation than our best Qwen3-1.7B-Base run.
• In our experiments, exact-match metrics were often stricter than execution-based metrics because semantically valid SQL rewrites do not always match the Spider gold form exactly.
• A later 4B rerun with altered training settings underperformed this checkpoint on Spider and is not the checkpoint published here.

Intended Use

This model is intended for:

• text-to-SQL research baselines
• schema-conditioned SQL generation experiments
• single-turn SQL generation from natural language plus schema text

It is not validated for:

• production-grade database access control
• unrestricted execution over arbitrary enterprise schemas
• multi-turn agent workflows without extra prompting / tooling

Example Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "bsq1989/qwen_4b_sql"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

prompt = """Generate SQL from the given schema and question. Output SQL only.

Schema:
CREATE TABLE twitter (TweetID INTEGER, UserID INTEGER, LocationID INTEGER, Lang TEXT, ...);
CREATE TABLE location (LocationID INTEGER, Country TEXT, City TEXT, ...);

Question:
How many tweets are in English?
"""

messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

• Performance drops on more open-ended and heterogeneous SQL benchmarks than Spider.
• The model can still produce invalid column references on out-of-distribution schemas.
• Benchmark numbers here reflect our current internal setup and should be reproduced with the same evaluation pipeline for strict comparison.