README.md

---
license: apache-2.0
base_model: unsloth/Llama-3.2-3B-Instruct
tags:
- text-to-sql
- nl2sql
- unsloth
- llama
- lora
- qlora
datasets:
- spider
metrics:
- exact_match
- similarity
model-index:
- name: querymind-nl2sql
  results: []
---

# 🧠 QueryMind: Natural Language to SQL Engine

QueryMind is a domain-specific, highly-optimized **NL-to-SQL engine** powered by a fine-tuned **LLaMA 3.2 3B Instruct** model. It has been fine-tuned using **QLoRA (4-bit)** via **Unsloth** on the **Spider NL2SQL dataset** to translate plain English queries into accurate, schema-valid SQL statements based on a provided database schema.

---

## 🎯 Model Details

- **Developed by:** Lakshitha Nuwan
- **Model type:** Causal Language Model (Fine-tuned LLM)
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct)
- **Training Framework:** Unsloth & PyTorch

---

## 🔗 Model Sources

- **HuggingFace Repository:** [lakshitha722/querymind-nl2sql](https://huggingface.co/lakshitha722/querymind-nl2sql)
- **Interactive Live Demo:** [HuggingFace Space Demo](https://huggingface.co/spaces/lakshitha722/querymind-nl2sql-demo)
- **Experiment Tracking:** [Weights & Biases (W&B) Dashboard](https://wandb.ai/lakshithanuwan722-other/querymind-nl2sql)

---

## 💻 How to Get Started with the Model

Use the code below to load the model and generate SQL queries using **Unsloth** (recommended for local GPUs) or standard HuggingFace **Transformers**.

### Inference with Unsloth (Recommended)

```python
from unsloth import FastLanguageModel
import torch

MODEL_NAME = "lakshitha722/querymind-nl2sql"

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name     = MODEL_NAME,
    max_seq_length = 1024,
    load_in_4bit   = True,
    dtype          = None,
)
FastLanguageModel.for_inference(model)

# 1. Define Prompt Template
PROMPT_TEMPLATE = """Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
Convert the following natural language question to a SQL query based on the given database schema. Return ONLY the SQL query, nothing else.

### Schema:
{schema}

### Question:
{question}

### Response:
"""

# 2. Prepare Inputs
schema = "Database: company\nTables: employees (id, name, department, salary, hire_date)"
question = "What is the average salary by department?"

prompt = PROMPT_TEMPLATE.format(schema=schema, question=question)
inputs = tokenizer([prompt], return_tensors="pt").to("cuda")

# 3. Generate
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens = 150,
        temperature    = 0.1,
        do_sample      = False,
        pad_token_id   = tokenizer.eos_token_id,
    )

# 4. Decode Output
input_length = inputs['input_ids'].shape[1]
sql = tokenizer.decode(outputs[0][input_length:], skip_special_tokens=True).strip()
print("Generated SQL:", sql)