---
language: 
  - en
tags:
  - text-to-sql
  - phi-3
  - genbi
  - dbt
  - azure-sql
  - qlora
base_model: microsoft/Phi-3-mini-4k-instruct
license: mit
---

# GenBI Phi-3 SQL Agent

## Model Description
This model is a specialized Small Language Model (SLM) designed to act as the reasoning engine for an Agentic GenBI Enterprise Platform. It translates natural language business questions into highly accurate, executable T-SQL queries. 

It has been instruction-tuned to understand modern data stack semantics, specifically bridging the gap between a **dbt Semantic Layer** and an **Azure SQL** data warehouse.

- **Developed by:** deepinfo
- **Model type:** Causal Language Model (Fine-tuned via QLoRA)
- **Base model:** Microsoft Phi-3-mini-4k-instruct
- **Primary Use Case:** Enterprise Business Intelligence, Autonomous SQL Generation, and LangGraph Agentic Workflows.

## Training Details
This model was fine-tuned using a synthetic dataset generated from a strictly typed `dbt` semantic layer (`manifest.json`). The training ensures the model adheres strictly to predefined business logic (e.g., Margin, Revenue, Customer Lifetime Value) rather than hallucinating column names or relationships.
- **Hardware:** NVIDIA T4/A100 (Google Colab)
- **Technique:** QLoRA (Quantized Low-Rank Adaptation)
- **Format:** Merged fp16/bf16 weights.

## Usage Example (Python)
```python
from transformers import pipeline

pipe = pipeline("text-generation", model="deepinfo/genbi-phi3-sql-agent")

prompt = """You are the reasoning engine for an Agentic GenBI Enterprise Platform. Your role is to translate business questions into accurate T-SQL queries for an Azure SQL database.

Context:
Target Table: fct_sales_performance
Columns:
- gross_revenue: Standard definition for total sales: Qty * Price.
- gross_profit: Margin definition: Revenue - Cost.
- category_name: The top-level classification of the product sold.

User: What was the total gross profit generated by the Bikes category?"""

output = pipe(prompt, max_new_tokens=100, temperature=0.1)
print(output[0]['generated_text'])