---
library_name: peft
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
pipeline_tag: text-generation
license: mit
language:
- en
datasets:
- xlangai/spider
tags:
- text-to-sql
- nl2sql
- lora
- qlora
- sft
- trl
---

# SQLForge — Qwen2.5-Coder-1.5B (text-to-SQL LoRA adapter)

A QLoRA adapter that fine-tunes [`Qwen/Qwen2.5-Coder-1.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct)
to translate natural-language questions into SQL, trained on the
[Spider](https://yale-lily.github.io/spider) dataset.

Evaluated with **execution accuracy** — every generated query is run against the
real SQLite database and the result set is compared to the gold query (not a
fragile string match).

## Results (full Spider dev set, 1034 examples)

| | Execution accuracy | Crashing queries |
|---|:---:|:---:|
| Base Qwen2.5-Coder-1.5B (zero-shot) | 57.45% | 228 |
| **+ this adapter** | **65.57%** | **148** |
| | **+8.1 pts** | **−35%** |

## Usage

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

base = "Qwen/Qwen2.5-Coder-1.5B-Instruct"
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, "Abdullahkousa2/sqlforge-qwen2.5-coder-1.5b")
tok = AutoTokenizer.from_pretrained(base)

messages = [
    {"role": "system", "content": "You are an expert data analyst. Given a SQLite "
     "database schema and a question, write a single valid SQLite SQL query that "
     "answers it. Respond with only the SQL query and nothing else."},
    {"role": "user", "content": 'Database schema:\nCREATE TABLE singer ("Name" text, "Age" int);\n\nQuestion: How many singers are there?'},
]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
out = model.generate(**tok(prompt, return_tensors="pt").to(model.device), max_new_tokens=128)
print(tok.decode(out[0], skip_special_tokens=True).split("assistant")[-1].strip())
# -> SELECT count(*) FROM singer
```

Or with the [`sqlforge`](https://pypi.org/project/sqlforge/) package:

```bash
pip install sqlforge
sqlforge -q "How many singers are there?" --db mydata.sqlite --run
```

## Training

- **Method:** QLoRA — 4-bit NF4 base + LoRA (r=16, α=32, dropout=0.05) on all attention + MLP projections
- **Schedule:** 3 epochs, lr 2e-4 cosine, effective batch size 16, bf16, paged AdamW 8-bit
- **Hardware:** a single RTX 3070 (8GB)

## Links

- 💻 **Code & training pipeline:** [github.com/abdullahkousa2/sqlforge](https://github.com/abdullahkousa2/sqlforge)
- 🤗 **Live demo:** [huggingface.co/spaces/Abdullahkousa2/sqlforge](https://huggingface.co/spaces/Abdullahkousa2/sqlforge)
- 📈 **Training run:** [Weights & Biases](https://wandb.ai/akousa360-arab-international-university-/sqlforge-text2sql)

## Limitations

A 1.5B model. Its main failure is *over-joining* — building an unnecessary JOIN and
referencing a column on the wrong table. Fine-tuning cut this by a third but didn't
eliminate it. State-of-the-art (~90%) requires a frontier model inside an agentic
pipeline; a locally-trained 1.5B realistically tops out in the 60s–70s.

## Framework versions

PEFT 0.19.1 · TRL 1.5.1 · Transformers 4.57.6 · PyTorch 2.7.0+cu128