Gregwdata

defog-sqlcoder-q8

gregwdata

12

defog-sqlcoder-q8 is a capable large language model created by gregwdata for converting natural language questions into SQL queries. It is a 15B parameter model fine-tuned on a base StarCoder model. Similar models include defog-sqlcoder-7b-2, a 7B parameter model that outperforms GPT-3.5-Turbo on natural language to SQL generation tasks, and sqlcoder2, a 15B parameter model that can outperform GPT-4 on certain SQL-related benchmarks when fine-tuned on a specific schema. Model inputs and outputs defog-sqlcoder-q8 takes in a natural language question and a description of the database schema, and generates SQL code to answer the question. The model can handle a wide variety of SQL query types, from simple SELECT statements to complex queries involving joins, aggregations, and subqueries. Inputs Prompt**: The natural language question to be converted to SQL Schema Metadata**: A description of the database schema, including the tables, columns, and relationships Seed**: An optional seed value for reproducible outputs Debug**: A flag to enable debug output Outputs SQL Query**: The generated SQL query that attempts to answer the input question Capabilities defog-sqlcoder-q8 is capable of converting a wide range of natural language questions into complex SQL queries. It can handle queries involving table joins, aggregations, calculations, and other advanced SQL concepts. The model has been evaluated on a benchmark dataset and is shown to outperform GPT-3.5-Turbo and other popular open-source models. What can I use it for? defog-sqlcoder-q8 can be used to build applications that allow users to interact with databases using natural language. This could include things like business intelligence tools, data exploration platforms, or even chatbots that can answer questions about data. The model could also be fine-tuned on a specific domain or data schema to further improve its performance. Things to try One interesting thing to try with defog-sqlcoder-q8 is to provide it with a complex natural language question and see how it breaks down the problem and generates the SQL query. Pay attention to how the model uses table aliases and handles different SQL constructs to answer the question accurately. You could also try providing the model with a variety of database schemas and see how its performance varies across different domains.

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