---
base_model:
- Qwen/Qwen3-4B-Instruct-2507
tags:
- nl2sql
- text-generation
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
datasets:
- xlangai/spider
---

# Qwen3 NL → SQL LoRA (Spider-style)

## Model Overview

- **Developed by:** Neeharika20  
- **Base model:** `Qwen/Qwen3-4B-Instruct-2507`  
- **Fine-tuning method:** LoRA  
- **Task:** Natural Language → SQL generation  
- **Frameworks:** Unsloth, TRL  
- **License:** Apache-2.0  

This model is a **LoRA adapter** fine-tuned to convert **natural language questions into SQL queries** given an explicit database schema.  
It follows the **Spider dataset prompt style** and is optimized for structured NL → SQL tasks rather than general chat.

Training was accelerated using **Unsloth**, enabling faster and memory-efficient fine-tuning.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

---

## Intended Use

### ✅ Recommended
- Natural Language → SQL generation
- Educational, research, and prototype systems
- Schema-aware SQL generation (Spider-style)

### ❌ Not Recommended
- General conversational chat
- Schema inference without explicit table definitions
- Production SQL execution without validation

---

## Reproducibility

This model can be reproduced by fine-tuning Qwen/Qwen3-4B-Instruct-2507 on the Spider dataset using Hugging Face TRL and Unsloth. Training was performed with a learning rate of 2e-4, max sequence length 2048, 4-bit quantization, and instruction-based prompting including schema context.

## Prompt
```
prompt = """
### Instruction:
Convert the question into an SQL query.

### Database Schema:
CREATE TABLE students(id INT, name TEXT, age INT);

### Question:
List the names of all students.

### SQL:
"""
```

## Limitations

- May hallucinate columns if schema is incomplete
- Performance drops on ambiguous schemas
- SQL syntax is not guaranteed to be valid in all edge cases
- No execution feedback loop during generation

## Ethical Considerations

- Trained exclusively on publicly available data
- No personal or sensitive data involved
- Generated SQL should be validated before execution
- Not designed for autonomous decision-making