---
license: cc-by-4.0
tags:
- Text-to-sql
library_name: transformers
---
# OGSQL-Mistral7B

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65fc0d4cae01a24b4004a011/Efvejrv0b9Vruks9ez6UU.png)

### Model Description
OGSQL-Mistral7B  was fine-tuned for the task of converting natural language text into SQL queries.


- **Model type**: Mixture Of Experts (MoE)
- **Language(s) (NLP)**: SQL (target language for generation)
- **Finetuned from model**: Mistral 7B instruct

## Use Case
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](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pQuIuCdoFMG76AH3BNZzep8PgRaZkkYS?usp=sharing)