---
base_model: Qwen/Qwen3-4B-Base
library_name: transformers
pipeline_tag: text-generation
tags:
  - text-to-sql
  - sql
  - qwen3
  - llamafactory
  - spider
  - spider-test-suite
---

# 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`](https://huggingface.co/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`](https://huggingface.co/datasets/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

```python
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.