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README.md

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+---
+license: cc-by-sa-4.0
+language:
+    - en
+base_model:
+    - defog/sqlcoder-7b-2
+tags:
+    - text-generation
+    - text-to-sql
+    - sql
+    - coding
+    - database
+    - instruction-following
+    - efficient-model
+---
+
+## SQLCoder-7B-2
+
+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](mailto:support@sandlogic.com) or visit [https://www.sandlogic.com/](https://www.sandlogic.com/)