| --- |
| 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)) |
| |
