Welcome to our guide on utilizing the powerful Code-Llama-2-13B-instruct-text2sql model for generating SQL queries from natural language questions! In this article, we will walk you through the process of leveraging this model effectively.

Understanding the Model

The Code-Llama-2-13B-instruct-text2sql is a fine-tuned version of Code Llama specifically designed for text-to-SQL tasks. Imagine it as a translator that converts your everyday language into the structured language of databases—SQL! With it, you can input a simple question about your data, and it will craft the perfect SQL query to extract the information you seek.

How to Use the Model

Here’s a step-by-step breakdown on how to use this model:

• Environment Setup: First, ensure you have the required libraries and a Python environment ready. You will need the transformers library.
• Import Required Modules: Import the necessary modules to get started by adding the following code:

from transformers import (    
    AutoModelForCausalLM,    
    AutoTokenizer,    
    pipeline)
import torch

• Load the Model: You must feed the model name to load it correctly. Use the code snippet below:

model_name = "bugdaryan/Code-Llama-2-13B-instruct-text2sql"
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

• Create Database Schema: Define your database schema as follows:

table = """
CREATE TABLE sales ( 
    sale_id number PRIMARY KEY, 
    product_id number, 
    customer_id number, 
    salesperson_id number, 
    sale_date DATE, 
    quantity number, 
    FOREIGN KEY (product_id) REFERENCES products(product_id), 
    FOREIGN KEY (customer_id) REFERENCES customers(customer_id), 
    FOREIGN KEY (salesperson_id) REFERENCES salespeople(salesperson_id)
);
"""

• Ask Questions: Formulate your question in natural language:

question = "Find the salesperson who made the most sales."

• Generate the SQL Query: Construct the prompt and obtain the SQL query by running this code:

prompt = f"[INST] Write SQLite query to answer the following question given the database schema. Please wrap your code answer using: Schema: {table} Question: {question} [INST]"
ans = pipe(prompt, max_new_tokens=100)
print(ans[0]['generated_text'].split()[2])

Example Use Cases

Here are some examples of natural language questions and their corresponding SQL queries generated by the model:

• What is the zip code in which the average mean sea level pressure is the lowest?
• Count the number of trips that did not end in San Francisco city.
• List the first names of people in alphabetical order.

Troubleshooting

If you encounter any issues while using the Code-Llama model, here are some common troubleshooting ideas:

• Model Not Found: Make sure the model name is correct and that your internet connection is stable to access the model.
• Performance Issues: Check whether your hardware meets the requirements for running the model, particularly the GPU specifications.
• Code Errors: Ensure that all library imports are correct and that Python is properly configured in your environment.

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Conclusion

Utilizing the Code-Llama-2-13B-instruct-text2sql model can significantly streamline your data querying processes, transforming your natural language questions into robust SQL commands. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.