Fine-Tuning Open-Source LLMs for Text-to-SQL: Results and Key Takeaways (article 3 of 3)

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.

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Published via Towards AI