Fine-Tune Factory LLMs for Continual Learning with Training Hub and PEFT

"""
Production implementation for Fine-Tuning LLMs using Training Hub and PEFT.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List
import os
import logging
import time
import requests
from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.orm import sessionmaker, declarative_base

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Database setup
Base = declarative_base()

class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///./test.db')

class ModelData(Base):
    __tablename__ = 'model_data'
    id = Column(Integer, primary_key=True)
    model_name = Column(String)
    training_data = Column(String)

# Create database engine and session
engine = create_engine(Config.database_url)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)

# Helper Functions

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'model_name' not in data:
        logger.error('Missing model_name')
        raise ValueError('Missing model_name')
    return True

async def fetch_data(url: str) -> List[Dict[str, Any]]:
    """Fetch training data from external API.
    
    Args:
        url: The URL to fetch data from
    Returns:
        Parsed JSON data
    Raises:
        HTTPError: If requests fail
    """  
    try:
        response = requests.get(url)
        response.raise_for_status()
        return response.json()
    except requests.HTTPError as e:
        logger.error(f'HTTP error occurred: {e}')
        raise

async def save_to_db(model_name: str, training_data: str) -> int:
    """Save model data to the database.
    
    Args:
        model_name: Name of the model
        training_data: Training data for the model
    Returns:
        ID of the saved model data
    Raises:
        Exception: If saving fails
    """  
    db = SessionLocal()
    try:
        model_entry = ModelData(model_name=model_name, training_data=training_data)
        db.add(model_entry)
        db.commit()
        db.refresh(model_entry)
        return model_entry.id
    except Exception as e:
        logger.error(f'Error saving to database: {e}')
        raise
    finally:
        db.close()

async def normalize_data(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Normalize fetched data.
    
    Args:
        data: List of raw data to normalize
    Returns:
        Normalized data
    """  
    normalized = []
    for item in data:
        normalized.append({
            'model_name': item.get('name', 'Unknown'),
            'training_data': item.get('data', '')
        })
    return normalized

async def aggregate_metrics(metrics: List[float]) -> float:
    """Aggregate training metrics.
    
    Args:
        metrics: List of metrics to aggregate
    Returns:
        Average of the metrics
    """  
    return sum(metrics) / len(metrics) if metrics else 0.0

async def handle_errors(func):
    """Decorator to handle exceptions and retries.
    
    Args:
        func: Function to wrap
    Returns:
        Wrapped function
    """  
    def wrapper(*args, **kwargs):
        for attempt in range(3):  # Retry logic
            try:
                return func(*args, **kwargs)
            except Exception as e:
                logger.warning(f'Attempt {attempt + 1} failed: {e}')
                time.sleep(2 ** attempt)  # Exponential backoff
        logger.error('Max retries exceeded')
        raise
    return wrapper

class FineTuneLLM:
    def __init__(self, model_url: str):
        self.model_url = model_url

    async def run(self) -> None:
        """Main workflow for fine-tuning LLM.
        
        Returns:
            None
        """  
        try:
            # Step 1: Fetch data
            raw_data = await fetch_data(self.model_url)
            logger.info('Fetched data successfully.')  

            # Step 2: Normalize data
            normalized_data = await normalize_data(raw_data)
            logger.info('Normalized data successfully.')  

            # Step 3: Save to DB
            for record in normalized_data:
                model_id = await save_to_db(record['model_name'], record['training_data'])
                logger.info(f'Saved model data with ID: {model_id}')  

        except Exception as e:
            logger.error(f'Error in the workflow: {e}')  

if __name__ == '__main__':
    # Example usage
    model_url = 'https://api.example.com/training-data'
    fine_tuner = FineTuneLLM(model_url)
    import asyncio
    asyncio.run(fine_tuner.run())