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))