SQLCoder-7B-2 is a specialized code generation model developed by Defog and optimized for Text-to-SQL generation, database querying, and structured data analysis workflows. This repository contains GGUF quantized variants of the model optimized for efficient local inference using llama.cpp.

The model is designed to translate natural language questions into syntactically correct SQL queries across a variety of database schemas. The quantized formats significantly reduce memory requirements while preserving strong SQL generation capability, enabling practical deployment on consumer hardware and edge environments.

Model Overview

Model Name: SQLCoder-7B-2
Base Model: defog/sqlcoder-7b-2
Architecture: Decoder-only Transformer
Parameter Count: 7 Billion
Modalities: Text
Primary Languages: English
Developer: Defog
License: CC BY-SA 4.0

Quantization Formats

This repository provides various GGUF quantized versions of the SQLCoder-7B-2 model, optimized for efficient local inference using llama.cpp. Below are the details of the available I-Matrix (IQ) formats.

IQ3_M

Size reduction of approx 76.89% (2.90 GB) compared to 16-bit (12.55 GB)
Aggressive 3-bit quantization optimized for maximum memory reduction
Suitable for low-memory systems and CPU-based inference
Enables lightweight deployment of Text-to-SQL workflows on constrained hardware
SQL generation quality may degrade on highly complex schemas, multi-table joins, and advanced analytical queries

IQ4_NL

Size reduction of approx 71.47% (3.58 GB) compared to 16-bit (12.55 GB)
Advanced 4-bit non-linear quantization designed to better preserve SQL generation quality
More suitable for structured query generation, schema understanding, and analytical database workflows
Typically provides stronger consistency and reduced quantization loss compared to lower-bit formats
Slightly increased computational overhead during inference

IQ4_XS

Size reduction of approx 72.91% (3.40 GB) compared to 16-bit (12.55 GB)
Balanced 4-bit quantization focused on efficiency and stable SQL generation performance
Good trade-off between model size, inference speed, and query quality
Suitable for general-purpose Text-to-SQL applications and local database assistants
Maintains reliable query generation behavior across most practical workloads

Training Background (Original Model)

SQLCoder-7B-2 is trained with a focus on natural language to SQL translation, database reasoning, schema understanding, and structured query generation.

Pretraining

Large-scale language model pretraining across diverse textual and code datasets
Focus on programming, structured reasoning, and database-related knowledge
Optimized for downstream SQL generation and analytical workflows

Instruction Tuning

Refined using Text-to-SQL datasets and schema-aware instruction tuning
Enhanced for generating syntactically correct SQL queries
Improved consistency for database querying, filtering, aggregation, and analytical operations

Key Capabilities

Text-to-SQL Generation Converts natural language requests into executable SQL queries.
Database Schema Understanding Interprets table structures, relationships, and schema context effectively.
SQL Query Construction Supports filtering, joins, aggregations, grouping, ordering, and analytical operations.
Efficient Local Deployment Quantized variants enable practical offline inference on consumer hardware.
Structured Data Analysis Assists with database exploration and business intelligence workflows.

Usage Example

Using llama.cpp

bash

./llama-cli \
  -m SandlogicTechnologies/sqlcoder-7b-2_IQ4_NL.gguf \
  -p "Generate a SQL query to find the top 10 customers by total revenue."

Recommended Usecases

Text-to-SQL Applications Build natural language interfaces for relational databases.
Database Assistants Generate and explain SQL queries from user requests.
Business Intelligence Workflows Assist with reporting, aggregation, and analytical query generation.
Data Exploration Simplify interaction with structured databases through natural language.
Research and Experimentation Evaluate Text-to-SQL performance and database reasoning workflows.

Acknowledgments

These quantized models are based on the original work by the Defog development team.

Special thanks to:

The Defog team for developing and releasing the SQLCoder-7B-2 model.
Georgi Gerganov and the llama.cpp open-source community for enabling efficient quantization and inference via the GGUF format.

Contact

For questions, feedback, or support, please reach out at support@sandlogic.com or visit https://www.sandlogic.com/

SandLogicTechnologies/sqlcoder-7b-2-GGUF

SQLCoder-7B-2