---
base_model: Qwen/Qwen3-4B-Base
library_name: peft
pipeline_tag: text-generation
language:
- en
tags:
- base_model:adapter:Qwen/Qwen3-4B-Base
- peft
- lora
- text-to-sql
- sql
- sft
- grpo
- rlvr
- trl
---

# SQL-R1

SQL-R1 trains Qwen3-4B-Base for complex Text-to-SQL through completion-only
supervised fine-tuning and GRPO with verifiable SQLite execution feedback.

This repository contains three LoRA adapters:

| Subfolder | Stage | Intended use |
|---|---|---|
| `sft` | Completion-only SFT | Supervised baseline |
| `grpo` | SFT + execution-feedback GRPO | Recommended main checkpoint |
| `grpo-hard` | Hard-sample curriculum GRPO | Ablation and analysis |

All three adapters require
[`Qwen/Qwen3-4B-Base`](https://huggingface.co/Qwen/Qwen3-4B-Base).

## Results

Execution accuracy (EX) and execution-valid rate were evaluated on the complete
public BIRD Dev and Spider Dev splits using a consistent greedy-decoding and
SQLite-execution pipeline.

| Checkpoint | BIRD Dev EX | BIRD execution-valid | Spider Dev EX | Spider execution-valid |
|---|---:|---:|---:|---:|
| Qwen3-4B-Base | 23.21% | 55.61% | 55.51% | 77.66% |
| `sft` | 42.37% | 87.29% | 77.27% | 96.62% |
| **`grpo`** | **42.37%** | 87.35% | **79.59%** | 97.00% |
| `grpo-hard` | 42.50% | **88.79%** | 78.92% | **97.20%** |

The `grpo` checkpoint improves Spider Dev EX by 2.32 percentage points over
SFT while preserving BIRD Dev EX. The curriculum ablation improves BIRD
execution validity but shows mild Spider forgetting.

These are project-side development-set measurements rather than hidden-test
leaderboard submissions. Systems using database-content retrieval,
self-consistency, reranking, or larger proprietary models are not directly
comparable.

## Training overview

### SFT

- Training records: 12,976
- Validation records: 608
- Completion-only loss: prompt tokens are masked and loss is computed only on
  the target SQL
- LoRA rank/alpha/dropout: 32 / 64 / 0.05
- Trainable parameters: 66.1M (1.62%)
- Precision: BF16

### GRPO

- RL training records: 12,885
- Optimization steps: 300
- Candidates per prompt: 4
- Approximate generated rollouts: 1,200
- Reward signals: SQL validity, read-only safety, executability, and execution
  result equivalence
- Parallel read-only SQLite rollout validation

Gold SQL and execution results are available only to the reward function and
are never included in the model prompt.

### Hard-sample curriculum ablation

- Curriculum records: 2,522
- BIRD moderate/challenging focus records: 1,891
- Stratified replay records: 631
- Optimization steps: 300
- Validation database overlap: 0

## Usage

Choose one of `sft`, `grpo`, or `grpo-hard`. The recommended default is `grpo`.

```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model_id = "Qwen/Qwen3-4B-Base"
repo_id = "j2521402/SQL-R1"
adapter_subfolder = "grpo"

tokenizer = AutoTokenizer.from_pretrained(
    repo_id,
    subfolder=adapter_subfolder,
)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(
    base_model,
    repo_id,
    subfolder=adapter_subfolder,
).eval()

messages = [
    {
        "role": "system",
        "content": (
            "You are a Text-to-SQL assistant. Given a SQLite database schema, "
            "optional evidence, and a question, return exactly one read-only "
            "SQLite query. Do not include explanations or Markdown fences."
        ),
    },
    {
        "role": "user",
        "content": (
            'Database schema:\nTABLE "singer" ("Singer_ID" INT, "Name" TEXT);\n\n'
            "Question:\nHow many singers do we have?"
        ),
    },
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to(model.device)

with torch.inference_mode():
    output = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=False,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.pad_token_id,
    )

completion = output[0, inputs["input_ids"].shape[1]:]
print(tokenizer.decode(completion, skip_special_tokens=True).strip())
```

Each subfolder also contains its tokenizer configuration and detailed model
card.

## Limitations

- Training and evaluation focus on English Text-to-SQL and SQLite.
- Prompts contain database schemas and optional evidence but do not
  automatically retrieve database contents.
- Execution equivalence is a strong but imperfect correctness signal.
- Generated SQL may still be wrong or expensive. Execute it only in a
  read-only sandbox with time and row limits.
- SQL-R1 is a direct Text-to-SQL model, not a general tool-calling agent.

## Code

Data processing, SFT, GRPO reward, training, and evaluation code:
[j2521402/SQL-R1](https://github.com/j2521402/SQL-R1).