Create README.md

README.md

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+---
+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'])