SQL Query Generation Model

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3 specialized for SQL query generation.

Model Details

Base Model: mistralai/Mistral-7B-Instruct-v0.3
Training Method: LoRA (Rank=16, Alpha=32)
Task: SQL query generation from natural language instructions
Training Framework: Unsloth

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load the model
tokenizer = AutoTokenizer.from_pretrained("exaler/aaa-2-sql-2")
model = AutoModelForCausalLM.from_pretrained("exaler/aaa-2-sql-2")

# Format your prompt
instruction = """You are an expert SQL query generator. Database schema:
Table: [dbo].[Users]
Columns: [ID], [Name], [Email], [CreatedDate]
Table: [dbo].[Orders]
Columns: [OrderID], [UserID], [Amount], [Status], [OrderDate]
"""

input_text = "Find all users who placed orders with amount greater than 1000"

prompt = f"<s>[INST] {instruction}\n\n{input_text} [/INST]"

# Generate SQL
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(inputs=inputs.input_ids, max_new_tokens=512, temperature=0.0)
response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)

Training Dataset

The model was trained on a custom dataset of SQL queries with their corresponding natural language descriptions.

Limitations

The model is optimized for the specific SQL database schema it was trained on
Performance may vary for database schemas significantly different from the training data