VPCSinfo/odoo17-sqlcoder-7b

Fine-Tuned SQLCoder-7B for Odoo 17

This is a fine-tuned version of defog/sqlcoder-7b-2 trained on Odoo 17 database schemas and queries.

Model Details

• Base model: defog/sqlcoder-7b-2
• Fine-tuned using LoRA for parameter-efficient adaptation
• Optimized for Odoo 17 SQL queries

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model with PEFT adapter
model = AutoModelForCausalLM.from_pretrained("VPCSinfo/odoo17-sqlcoder-7b")
tokenizer = AutoTokenizer.from_pretrained("VPCSinfo/odoo17-sqlcoder-7b")

# Example query
schema = """

CREATE TABLE res_partner (
    id INTEGER PRIMARY KEY, 
    name VARCHAR(255),
    email VARCHAR(255),
    phone VARCHAR(64)
);
CREATE TABLE sale_order (
    id INTEGER PRIMARY KEY,
    partner_id INTEGER REFERENCES res_partner(id),
    date_order TIMESTAMP,
    state VARCHAR(20),
    amount_total NUMERIC
);

"""

question = "Find all customers who have orders with a total amount greater than 1000"

# Format your input following SQLCoder's expected format
prompt = f'''### Task
Generate a SQL query to answer the question below based on the table schema.

### Database Schema

CREATE TABLE res_partner (
    id INTEGER PRIMARY KEY, 
    name VARCHAR(255),
    email VARCHAR(255),
    phone VARCHAR(64)
);
CREATE TABLE sale_order (
    id INTEGER PRIMARY KEY,
    partner_id INTEGER REFERENCES res_partner(id),
    date_order TIMESTAMP,
    state VARCHAR(20),
    amount_total NUMERIC
);


### Question
Find all customers who have orders with a total amount greater than 1000

### SQL Query
'''

# Generate SQL
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=300)
sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True).split("### SQL Query")[1].strip()
print(sql_query)