sqlcoder-7b-2
Maintainer: defog
245
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The sqlcoder-7b-2 model is a capable large language model for natural language to SQL generation, developed by Defog, Inc. It is a fine-tuned model from the CodeLlama-7B base model, and builds on the previous sqlcoder-70b-alpha and sqlcoder models. The model has been shown to outperform all generalist models, including GPT-4, on text-to-SQL tasks.
Model inputs and outputs
Inputs
- Natural language questions: The model takes in natural language questions about data stored in a database as input.
Outputs
- SQL queries: The model generates SQL queries that answer the provided natural language question, based on the given database schema.
Capabilities
The sqlcoder-7b-2 model is highly capable at generating SQL queries from natural language. It has been shown to perform particularly well on tasks involving joins, with a 97.1% accuracy on join-related queries. The model can also handle a variety of other SQL tasks such as group-by, order-by, ratio calculations, and where clauses.
What can I use it for?
The sqlcoder-7b-2 model is intended to be used by non-technical users to understand and query data stored in SQL databases. It can be a useful analytics tool, allowing users to explore their data by asking natural language questions without requiring SQL expertise. The model could be integrated into business intelligence or data exploration applications to provide a more accessible interface for accessing and understanding data.
Things to try
One key aspect of the sqlcoder-7b-2 model is its focus on alignment and safety. The model was trained and evaluated using the SQL-Eval framework, which was developed by Defog to test and ensure the model's capabilities and alignment. This suggests the model may be well-suited for use cases where safety and responsible deployment are important considerations, such as in enterprise or regulated environments.
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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