sqlcoder-7b

Maintainer: defog

Total Score

59

Last updated 5/28/2024

💬

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

sqlcoder-7b is a 7 billion parameter model developed by Defog that is designed for converting natural language questions into SQL queries. It is a state-of-the-art language model that outperforms popular open-source models like GPT-3.5 and even GPT-4 on natural language to SQL generation tasks. The model is fine-tuned on a base Mistral-7B model.

Compared to similar models like [object Object] and [object Object], sqlcoder-7b has slightly lower performance but consumes fewer GPU resources, making it more accessible for users with less powerful hardware. The maintainer, Defog, has also developed larger models like sqlcoder2 and sqlcoder-34b-alpha that offer even better performance.

Model inputs and outputs

Inputs

  • Natural language question: The model takes as input a natural language question about data stored in a database.

Outputs

  • SQL query: The model outputs a SQL query that can be used to retrieve the data to answer the input question.

Capabilities

sqlcoder-7b is highly capable at translating natural language questions into accurate SQL queries. It performs particularly well on questions involving group-by, order-by, and date-based operations, outperforming GPT-4 and other popular models. The model also handles complex queries involving joins and ratio calculations effectively.

What can I use it for?

You can use sqlcoder-7b as an analytics tool to empower non-technical users to explore data stored in SQL databases. By allowing users to ask questions in plain language and generating the corresponding SQL queries, the model can make data more accessible and enable faster insights.

This model could be particularly useful for customer-facing applications, business intelligence tools, or data exploration platforms where end-users need to query data without writing SQL directly.

Things to try

Try providing the model with a variety of natural language questions covering different database schema and query types. Observe how the model performs on complex queries involving aggregations, joins, and advanced SQL constructs. You can also experiment with fine-tuning the model on your own dataset to improve its performance on your specific use case.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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