Enhancing Text-to-SQL with a Fine-Tuned 7B LLM for Database Interactions

A Step-by-Step Guide to Fine-Tuning Models for SQL Generation on Custom Database Structures

I have encountered challenges when using 7B LLMs for SQL generation tasks, particularly when working with the company’s databases. These models often struggle to generate accurate SQL queries, even when provided with the database schema and table relationships in the context. To address this challenge, fine-tuning a 7B model using QLoRA on a custom dataset tailored to your specific database schema is an effective approach.

In this article, I’ll walk you through the process of fine-tuning a 7B model to handle SQL generation tasks more effectively, and how you can integrate the fine-tuned model into a LangChain-based application for real-time database interactions.

Overview

Key steps outline

Before we dive into the details, let’s outline the key steps we’ll be following in this guide:

1. Prepare a custom dataset based on a database schema.
2. Fine-tune the 7B model using QLoRA technique.
3. Evaluate the performance of the fine-tuned model.
4. Integrate the model into a LangChain application for SQL-based database interaction.

By following this guide, you’ll be able to build a question-answering application for SQL databases using a fine-tuned Mistral 7B model, optimized for generating SQL queries based on your specific database schema.

Step 1: Preparing Your Custom Dataset

To fine-tune the model effectively, you need a high-quality dataset that reflects your database’s structure. Let’s consider a simple customer management database with the following tables:

1. Customer
2. Address
3. Contact

Sample DDL for Customer Table

CREATE TABLE customer (
    customer_key INT PRIMARY KEY,
    source…