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.

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

Neeharika20/qwen-spider-lora

Qwen3 NL → SQL LoRA (Spider-style)

Model Overview