---
language:
  - en
license: apache-2.0
library_name: peft
tags:
  - text-generation
  - sql
  - chat
  - peft
  - lora
  - transformers
  - phi-3
  - instruction-tuning
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
pipeline_tag: text-generation
inference: false
---

# 🧠 SQL Chat – Phi-3-mini SQL Assistant

**Model ID:** `saadkhi/SQL_Chat_finetuned_model`  
**Base model:** `unsloth/Phi-3-mini-4k-instruct-bnb-4bit`  
**Model type:** LoRA (merged)  
**Task:** Natural Language → SQL query generation + conversational SQL assistance  
**Language:** English  
**License:** Apache 2.0

This model is a fine-tuned version of **Phi-3-mini-4k-instruct** (4-bit quantized) specialized in understanding natural language questions about databases and generating correct, clean SQL queries.

## ✨ Key Features

- Very good balance between size, speed and SQL generation quality  
- Works well with common database dialects (PostgreSQL, MySQL, SQLite, SQL Server, etc.)  
- Can explain queries, suggest improvements and handle follow-up questions  
- Fast inference even on consumer hardware (especially with 4-bit quantization)

## 🎯 Intended Use & Capabilities

**Best for:**
- Converting natural language questions → SQL queries  
- Helping beginners learn SQL through explanations  
- Quick prototyping of SQL queries in development  
- Building SQL chat interfaces / tools / assistants  
- Educational purposes

**Limitations / Not recommended for:**
- Extremely complex analytical/business intelligence queries  
- Real-time query optimization advice  
- Very database-specific or proprietary SQL extensions  
- Production systems without human review (always validate generated SQL!)

## 🛠️ Quick Start (merged LoRA version)

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

model_id = "saadkhi/SQL_Chat_finetuned_model"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

# Simple prompt style (chat template is recommended)
prompt = """Show all customers who placed more than 5 orders in 2024"""

messages = [{"role": "user", "content": prompt}]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=180,
    do_sample=False,
    temperature=0.0,
    pad_token_id=tokenizer.eos_token_id
)

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