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.