Fine-Tune Industrial Domain LLMs from YAML Config with LLaMA-Factory and PEFT

                      
                     
"""
Production implementation for Fine-Tuning Industrial Domain LLMs.
Utilizes LLaMA-Factory and PEFT for efficient model training.
"""

from typing import Dict, Any, List
import os
import logging
import time
import yaml
from pydantic import BaseModel, ValidationError
from contextlib import contextmanager

# Setting up logger for various levels of information
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config(BaseModel):
    """Configuration class for environment variables and model parameters."""
    model_name: str = os.getenv('MODEL_NAME', 'default_model')
    epochs: int = int(os.getenv('EPOCHS', '3'))
    batch_size: int = int(os.getenv('BATCH_SIZE', '32'))
    learning_rate: float = float(os.getenv('LEARNING_RATE', '0.001'))

@contextmanager
def model_session():
    """Context manager for managing model resources."""
    try:
        # Initialize and return model resources
        logger.info('Initializing model session.')
        yield
    finally:
        # Cleanup resources
        logger.info('Closing model session.')

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

async def load_config(file_path: str) -> Config:
    """Load configuration from YAML file.
    
    Args:
        file_path: Path to the YAML config file
    Returns:
        Config: Parsed configuration object
    Raises:
        FileNotFoundError: If file does not exist
        yaml.YAMLError: If there's an error in the YAML file
    """
    if not os.path.exists(file_path):
        raise FileNotFoundError(f'Config file not found: {file_path}')
    with open(file_path, 'r') as file:
        config_data = yaml.safe_load(file)
    return Config(**config_data)

async def fetch_data(dataset_path: str) -> List[Dict[str, Any]]:
    """Fetch dataset from specified path.
    
    Args:
        dataset_path: Path to the dataset file
    Returns:
        List[Dict[str, Any]]: List of data records
    Raises:
        FileNotFoundError: If dataset file does not exist
    """
    if not os.path.exists(dataset_path):
        raise FileNotFoundError(f'Dataset not found: {dataset_path}')
    # Simulating data fetching
    logger.info('Fetching data from dataset.')
    return [{'input': 'sample input', 'output': 'sample output'}]  # Placeholder data

async def save_to_db(data: Any) -> None:
    """Save processed data to database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If save operation fails
    """
    try:
        logger.info('Saving data to database.')
        # Simulate save operation
        time.sleep(0.5)  # Simulating delay
    except Exception as e:
        logger.error(f'Failed to save data: {e}')
        raise

async def train_model(config: Config, training_data: List[Dict[str, Any]]) -> None:
    """Train the model based on provided configuration and data.
    
    Args:
        config: Configuration object
        training_data: List of training data records
    """
    logger.info(f'Training model: {config.model_name} for {config.epochs} epochs.')
    # Simulate training process
    for epoch in range(config.epochs):
        logger.info(f'Epoch {epoch + 1}/{config.epochs} started.')
        time.sleep(1)  # Simulating training time
        logger.info(f'Epoch {epoch + 1} completed.')

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

if __name__ == '__main__':
    # Example usage block
    try:
        config = load_config('config.yaml')
        with model_session():
            data = fetch_data('training_data.json')
            await validate_input(data)
            await train_model(config, data)
            await save_to_db(data)
    except (ValueError, FileNotFoundError, yaml.YAMLError) as e:
        logger.error(f'Error occurred: {e}')