Fast and Memory-Efficient Text-to-SQL with Qwen2.5 Coder 32B Instruct on Your GPU
Quantization and prompting with vLLM
Large language models (LLMs) have proven to be highly effective across a wide range of tasks. Among these, coding stands out as one of the most popular applications. With tools like GitHub Copilot, Cursor, and even simpler platforms like Google Colab, coding tools are increasingly integrating the powerful capabilities of LLMs.
These models are very good at tasks such as generating code from natural language queries, commenting on code, identifying and explaining bugs, and completing partially written code. By automating or assisting with these tasks, LLMs can significantly accelerate the coding process.
One of the most sought-after coding applications for LLMs is writing SQL queries. For many developers, crafting SQL queries can be tedious and requires a thorough understanding of the database structure to ensure efficiency. Thankfully, modern open LLMs have shown exceptional performance in this area.
State-of-the-art coder models, such as Qwen2.5 Coder 32B Instruct, are specifically trained on extensive SQL datasets, making them highly proficient SQL assistants.
In this article, we will explore how to leverage a coder model like Qwen2.5 Coder to write SQL queries for a given database. We will begin by reviewing how Qwen2.5 Coder was trained, which will help us understand its capabilities and limitations. Then, we’ll dive into practical examples of using the model for SQL-related tasks, paying close attention to the memory and efficiency considerations. While the full 32B version of Qwen2.5 Coder requires substantial computational resources and cannot run on a standard consumer GPU, a 4-bit quantized version provides an efficient alternative without sacrificing performance for SQL tasks.
The following notebook demonstrates examples of how to use Qwen2.5 Coder for text-to-SQL tasks effectively: