Text2sql 1.5B

🚀 Text2SQL-1.5B Model

Text2SQL-1.5B is a powerful natural language to SQL model that can convert user queries into structured SQL statements. It supports complex multi - table queries and ensures high accuracy in text - to - SQL conversion.

🚀 Quick Start

Overview

This model is designed to transform natural language queries into SQL statements. It can handle complex multi - table queries with high accuracy.

System Instruction

To ensure consistency in model outputs, use the following system instruction:

⚠️ Important Note

Always separate code and explanation. Return SQL code in a separate block, followed by the explanation in a separate paragraph. Use markdown triple backticks (```sql for SQL) to format the code properly. Write the SQL query first in a separate code block. Then, explain the query in plain text. Do not merge them into one response.

For json result use the following:

⚠️ Important Note

Always separate SQL code and explanation. Return SQL queries in a JSON format containing two keys: 'query' and 'explanation'. The response should strictly follow the structure: {"query": "SQL_QUERY_HERE", "explanation": "EXPLANATION_HERE"}. The 'query' key should contain only the SQL statement, and the 'explanation' key should provide a plain - text explanation of the query. Do not merge them into one response.

Prompt Format

The prompt format should include both the user query and the table structure using a CREATE TABLE statement. The expected message format should be:

messages = [
    {"role": "system", "content": "Always separate code and explanation. Return SQL code in a separate block, followed by the explanation in a separate paragraph. Use markdown triple backticks (```sql for SQL) to format the code properly. Write the SQL query first in a separate code block. Then, explain the query in plain text. Do not merge them into one response. The query should always include the table structure using a CREATE TABLE statement before executing the main SQL query."},
    {"role": "user", "content": "Show the total sales for each customer who has spent more than $50,000."},
    {"role": "user", "content": "CREATE TABLE sales (id INT PRIMARY KEY, customer_id INT, total_amount DECIMAL(10,2), FOREIGN KEY (customer_id) REFERENCES customers(id)); CREATE TABLE customers (id INT PRIMARY KEY, name VARCHAR(255))"}
] 

💻 Usage Examples

Basic Usage

The following code demonstrates how to use the model to convert natural language queries into SQL statements:

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/Text2SQL-1.5B")
model = AutoModelForCausalLM.from_pretrained("yasserrmd/Text2SQL-1.5B")

# Define the pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

# Define system instruction
system_instruction = "Always separate code and explanation. Return SQL code in a separate block, followed by the explanation in a separate paragraph. Use markdown triple backticks (```sql for SQL) to format the code properly. Write the SQL query first in a separate code block. Then, explain the query in plain text. Do not merge them into one response. The query should always include the table structure using a CREATE TABLE statement before executing the main SQL query."

# Define user query
user_query = "Show the total sales for each customer who has spent more than $50,000. CREATE TABLE sales (id INT PRIMARY KEY, customer_id INT, total_amount DECIMAL(10,2), FOREIGN KEY (customer_id) REFERENCES customers(id)); CREATE TABLE customers (id INT PRIMARY KEY, name VARCHAR(255))"

# Define messages for input
messages = [
    {"role": "system", "content": system_instruction},
    {"role": "user", "content": user_query},
]

# Generate SQL output
response = pipe(messages)

# Print the generated SQL query
print(response[0]['generated_text'])

📚 Documentation

Uploaded model

• Developed by: yasserrmd
• License: apache - 2.0
• Finetuned from model : unsloth/qwen2.5 - coder - 1.5b - instruct - bnb - 4bit

This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Model Information