Fine-Tuning Open-Source LLMs for Text-to-SQL: Results and Key Takeaways (article 3 of 3)
Last Updated on August 28, 2025 by Editorial Team
Author(s): Lorentz Yeung
Originally published on Towards AI.
Fine-Tuning Open-Source LLMs for Text-to-SQL: Results and Key Takeaways (article 3 of 3)
In my text-to-SQL fine-tuning project, I tried to push open-source LLMs like Llama 3.1 8B Instruct and Qwen 2.5 series to their limits using GRPO.
The article details the author’s extensive testing of open-source language models for text-to-SQL tasks, highlighting a significant focus on executing complex SQL queries while documenting successes and failures over a rigorous three-month period. It emphasizes the need for better training methodologies and additional resources to improve model performance, particularly on hard queries. The author aspires to provide insights that will aid others in successfully fine-tuning these models while avoiding common pitfalls.
Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.