| ---
|
| 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']) |
