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DuckDB-NSQL-7B-v0.1 is an autoregressive open-source large foundation model (FM) designed specifically for SQL generation tasks. It is based on Meta's original Llama-2 7B model and further pre-trained on a dataset of general SQL queries, then fine-tuned on a dataset of DuckDB text-to-SQL pairs. This model is part of the NSQL family of models from motherduckdb. It aims to outperform existing text-to-SQL models by generating valid DuckDB SQL statements beyond just SELECT queries. The model was trained on 200k DuckDB text-to-SQL pairs, synthetically generated and from the NSText2SQL dataset. Model Inputs and Outputs Inputs Natural language instructions or questions about data in a DuckDB database Outputs Valid DuckDB SQL statements to answer the given input prompt, which may include complex queries beyond just SELECT statements. Capabilities The DuckDB-NSQL-7B-v0.1 model has been designed to handle a wide range of SQL generation tasks for DuckDB databases. Unlike traditional text-to-SQL models, it can generate any valid DuckDB SQL statement, including those for official DuckDB extensions, not just simple SELECT queries. For example, the model can generate SQL to create new tables, insert data, update records, and more, in addition to complex analytical queries. This makes it a versatile tool for working with DuckDB databases, beyond just querying the data. What Can I Use It For? The DuckDB-NSQL-7B-v0.1 model is well-suited for building applications and tools that interact with DuckDB databases using natural language. This could include: Developing conversational interfaces for DuckDB data analysis Automating DuckDB database management tasks through natural language commands Integrating DuckDB functionality into no-code/low-code platforms Enhancing business intelligence and data exploration workflows By leveraging the model's capabilities to generate complex DuckDB SQL, developers can create more powerful and user-friendly data-driven applications. Things to Try One interesting aspect of the DuckDB-NSQL-7B-v0.1 model is its ability to generate SQL statements beyond just SELECT queries. Try providing the model with prompts that require complex database operations, such as: Creating a new table from a CSV file Updating multiple records based on a filter condition Performing joins and aggregations across multiple tables Calling DuckDB extension functions in the generated SQL Observe how the model handles these more advanced SQL use cases and see if it can generate correct and effective solutions. This can help you understand the limits of the model's capabilities and explore new ways to leverage it in your DuckDB-powered applications.

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Updated 5/16/2024