README.md

metadata

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)

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))