SQLCoder: a state-of-the-art LLM for SQL generation

  •   SQLCoder, an open-source product by Defog, converts natural language questions into SQL queries.
  •   It surpasses the performance of many open-source models, even edging out models like gpt-3.5-turbo and text-davinci-003 which are 10 times its size.
  •   You can test SQLCoder using the provided interactive demo.

Technical Details

  •   SQLCoder is a 15B parameter Language Learning Model (LLM) that's a refined version of StarCoder.
  •   It's optimized for hand-crafted SQL queries of varying complexity.
  •   On certain individual database schemas, SQLCoder rivals or even surpasses GPT-4 in performance.


  •   Over the past three months, enterprises in healthcare, finance, and government have used SQLCoder.
  •   The primary advantage: it can be self-hosted, ensuring sensitive data stays on the server.
  •   The release is Defog's way of contributing back to the community, given they built upon existing models like StarCoder.


  •   Defog crafted a unique dataset centered on text-to-SQL tasks derived from 10 varied schemas. An additional evaluation dataset was produced from 7 new schemas.
  •   The dataset's complexity was ensured by selecting intricate schemas comprising 4-20 tables.
  •   Each question was categorized based on difficulty, using a method inspired by the Spider dataset.
  •   The model fine-tuning process was split into two stages, beginning with simpler questions, leading up to the more complex ones.


  •   Assessing the accuracy of SQL queries is inherently tricky due to multiple valid solutions for a single query.
  •   Therefore, Defog had to create a unique framework to gauge the correctness of SQL queries. They've open-sourced this framework and the accompanying dataset.


  •   SQLCoder excels against all notable models, save for GPT-4, based on Defog's evaluation mechanism.
  •   Especially, it bests some models that are much larger in size.
  •   For specific database schemas, its performance and responsiveness match or surpass OpenAI's GPT-4.

Future Prospects

  • Defog plans to enhance SQLCoder by:
  • Incorporating more curated data and broader questions.
  • Utilizing advanced training techniques like Reward Modeling and RLHF.
  • Introducing a specialized model for data analysis combining SQL and Python.


  The model can be explored and tested via Defog's interactive demo.

This summary encapsulates the primary features, approach, and future plans for SQLCoder by Defog.


SQL Coder Model

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