Fine-tuning

This model was fine-tuned on a custom T-SQL dataset using QLoRA. The goal was to adapt a Phi-4 Mini (base) model to better handle Microsoft SQL Server query generation tasks while keeping training lightweight and efficient.

Approach

• Base model: Phi-4 Mini (base)
  
• Method: QLoRA
  
• Training data: Custom instruction-style T-SQL dataset
  
• Target domain: Microsoft SQL Server / T-SQL tasks
  

The training setup was designed to improve the model’s ability to respond to schema-aware SQL prompts, generate readable T-SQL queries, and follow common query-writing patterns.

Dataset Format

The fine-tuning dataset used instruction-response examples, typically including:

• Table definitions or schema context
  
• A natural language query request
  
• The expected T-SQL output
  

This format helps the model learn how to map structured database context and user instructions into SQL queries.

Notes

• The model is intended for query assistance, not guaranteed correctness
  
• Outputs should be reviewed before use in real systems
  
• Performance may vary depending on schema complexity and prompt quality