OGSQL-7B is designed to facilitate the conversion of natural language queries into structured SQL commands, aiding in database querying without the need for manual SQL knowledge.

How to Get Started with the Model

python

# Example code to load and use the model
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_name = "OGSQL-Mistral7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

def generate_sql(query):
    inputs = tokenizer.encode(query, return_tensors="pt")
    outputs = model.generate(inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example use
query = """
using this context:
-- Create Customers Table
CREATE TABLE Customers (
    customer_id INTEGER PRIMARY KEY,
    name TEXT NOT NULL,
    email TEXT,
    join_date DATE
);

-- Create Products Table
CREATE TABLE Products (
    product_id INTEGER PRIMARY KEY,
    name TEXT NOT NULL,
    price DECIMAL(10, 2)
);

-- Create Orders Table
CREATE TABLE Orders (
    order_id INTEGER PRIMARY KEY,
    customer_id INTEGER,
    product_id INTEGER,
    order_date DATE,
    quantity INTEGER,
    total_price DECIMAL(10, 2),
    FOREIGN KEY (customer_id) REFERENCES Customers(customer_id),
    FOREIGN KEY (product_id) REFERENCES Products(product_id)
);

show me all the orders from last month , sort by date


"""
print(generate_sql(query))

alternatively you can use this notebook:

Colab notebook

OneGate/OGSQL-Mistral-7B

OGSQL-Mistral7B

Model Description

Use Case

How to Get Started with the Model

alternatively you can use this notebook:

OneGate/
OGSQL-Mistral-7B