These models turn a plain-language question into a SQL query so managers can pull numbers from a database without an analyst. Quality depends heavily on how clearly your table structure is described. Check support for your database engine, the license, and plan for read-only access.
A text-to-SQL model of just 1B parameters, designed to run locally so the database never leaves for external services.
A text-to-SQL model trained with reinforcement learning: it works through the schema and the conditions step by step before producing a query.
A Snowflake model for turning questions into SQL, trained with reinforcement learning by checking query results. The open 7B version is based on Qwen2.5-Coder.
Alibaba models for turning questions into SQL, based on Qwen2.5-Coder. They work with different SQL dialects; a small 3B version suits modest hardware.
Models for turning questions into SQL, trained on millions of synthetic query examples across different databases. Three sizes for different hardware.
One of the first open models that turn a plain-language question into an SQL query against a database. Available in Ollama, but newer competitors are already stronger.
A text-to-SQL model from the open Chat2DB database client: it supports different SQL dialects, with an English and Chinese model card.
A text-to-SQL model built on DeepSeek-Coder, aimed at complex questions spanning several tables and conditions.
A model for turning questions into SQL, built for the embedded analytics database DuckDB. Available in Ollama, convenient for working with CSV and Parquet locally.
An early line of open text-to-SQL models starting at 1B, including variants fine-tuned for specific database schemas.
One of the first open text-to-SQL lines, including very small versions from 350M that run on an ordinary PC.
An open model can run on your own server: data stays in-house, there is no per-request fee, and the model can be fine-tuned on your documents.
