llama-3-sqlcoder-8b
| Property | Value |
|---|---|
| Parameter Count | 8.03B |
| Model Type | Text-to-SQL Generation |
| License | CC-BY-SA-4.0 |
| Base Model | Meta-Llama-3-8B-Instruct |
| Tensor Type | BF16 |
What is llama-3-sqlcoder-8b?
llama-3-sqlcoder-8b is a specialized language model developed by Defog, Inc. for converting natural language questions into SQL queries. Built on the Meta-Llama-3-8B-Instruct architecture, it's specifically optimized for generating queries compatible with PostgreSQL, Redshift, and Snowflake databases. The model represents a significant advancement in making database interactions more accessible through natural language processing.
Implementation Details
The model implements a sophisticated text-to-SQL generation pipeline, utilizing 8.03 billion parameters and BF16 tensor precision. It's designed to work with zero temperature settings and no sampling for optimal performance, ensuring deterministic and precise SQL query generation.
- Optimized prompt structure with clear delineation of user inputs and SQL outputs
- Evaluated using SQL-Eval, a PostgreSQL-based testing framework
- Supports complex query generation across multiple database platforms
- Implements strict evaluation metrics for accuracy and reliability
Core Capabilities
- Natural language to SQL query conversion
- Support for PostgreSQL, Redshift, and Snowflake syntax
- Frontier-level performance in query generation accuracy
- Structured DDL statement processing
- Zero-shot query generation capabilities
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its specialized focus on SQL generation, matching the performance of larger generalist models while maintaining efficiency with its 8B parameter size. It's specifically optimized for real-world database interactions across multiple platforms.
Q: What are the recommended use cases?
The model is ideal for developers and data analysts who need to convert natural language questions into SQL queries, particularly in PostgreSQL, Redshift, or Snowflake environments. It's best used with temperature set to 0 for deterministic results.
