---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
pipeline_tag: text-generation
tags:
- text-generation
- sql-generation
- llama
- lora
- peft
- unsloth
- transformers
license: apache-2.0
language:
- en
---

# SQL-Genie (LLaMA-3.1-8B Fine-Tuned)

## 🧠 Model Overview

**SQL-Genie** is a fine-tuned version of **LLaMA-3.1-8B**, specialized for converting **natural language questions into SQL queries**.

The model was trained using **parameter-efficient fine-tuning (LoRA)** on a structured SQL instruction dataset, enabling strong SQL generation performance while remaining lightweight and affordable to train on limited compute (Google Colab).

- **Developed by:** dhashu  
- **Base model:** `unsloth/meta-llama-3.1-8b-bnb-4bit`  
- **License:** Apache-2.0  
- **Training stack:** Unsloth + Hugging Face TRL  

---

## ⚙️ Training Methodology

This model was trained using **LoRA (Low-Rank Adaptation)** via the **PEFT** framework.

### Key Details
- Base model loaded in **4-bit quantization** for memory efficiency
- **Base weights frozen**
- **LoRA adapters** applied to:
  - Attention layers (`q_proj`, `k_proj`, `v_proj`, `o_proj`)
  - Feed-forward layers (`gate_proj`, `up_proj`, `down_proj`)
- Fine-tuned using **Supervised Fine-Tuning (SFT)**

This approach allows efficient specialization without full model retraining.

---

## 📊 Dataset

The model was trained on a subset of the **`b-mc2/sql-create-context`** dataset, which includes:

- Natural language questions
- Database schema / context
- Corresponding SQL queries

Each sample was formatted as an **instruction-style prompt** to improve reasoning and structured output.

---

## 🚀 Performance & Efficiency

- 🚀 **2× faster fine-tuning** using Unsloth
- 💾 **Low VRAM usage** via 4-bit quantization
- 🧠 Improved SQL syntax and schema understanding
- ⚡ Suitable for real-time inference and lightweight deployments

---

## 🧩 Model Variants

This repository contains a **merged model**:

### 🔹 Merged 4-bit Model
- LoRA adapters merged into base weights
- No PEFT required at inference time
- Ready-to-use single checkpoint
- Optimized for easy deployment

---

## ▶️ How to Use (Inference)

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "dhashu/sql-genie-full"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    load_in_4bit=True,
)

prompt = """Below is an input question, context is given to help. Generate a SQL response.
### Input: List all employees hired after 2020
### Context: CREATE TABLE employees(id, name, hire_date)
### SQL Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=128,
    temperature=0.7,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))