Fine-Tune Domain-Specific LLMs with LLaMA-Factory and Axolotl for Manufacturing Workflows

                      
                     
"""
Production implementation for fine-tuning domain-specific LLMs using LLaMA-Factory and Axolotl.
Provides secure, scalable operations tailored for manufacturing workflows.
"""
from typing import Dict, Any, List
import os
import logging
import time
import requests
from contextlib import contextmanager

# Set up logging configuration
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class for environment variables."""
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///default.db')
    api_key: str = os.getenv('API_KEY')

@contextmanager
def resource_manager():
    """Context manager for resource handling, like database connections."
    try:
        # Simulate resource allocation
        logger.info('Allocating resources...')
        yield
    finally:
        # Simulate resource cleanup
        logger.info('Releasing resources...')

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

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent security issues.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {k: v.strip() for k, v in data.items()}  # Strips whitespace

async def fetch_data(api_endpoint: str) -> Dict[str, Any]:
    """Fetch training data from an external API.
    
    Args:
        api_endpoint: URL of the API to fetch data from
    Returns:
        Fetched data as a dictionary
    Raises:
        HTTPError: If the request fails
    """
    response = requests.get(api_endpoint)
    response.raise_for_status()  # Raise HTTPError for bad responses
    return response.json()

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save the model training data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If saving fails
    """
    try:
        logger.info('Saving data to the database...')
        # Simulate database save operation
        pass  # Replace with actual DB save code
    except Exception as e:
        logger.error(f'Error saving to DB: {e}')
        raise

async def call_api(endpoint: str, payload: Dict[str, Any]) -> Dict[str, Any]:
    """Call an external API with payload.
    
    Args:
        endpoint: API endpoint to call
        payload: Data to send
    Returns:
        Response data
    Raises:
        Exception: If API call fails
    """
    response = requests.post(endpoint, json=payload)
    response.raise_for_status()  # Raise error for bad responses
    return response.json()

async def process_batch(batch: List[Dict[str, Any]]) -> None:
    """Process a batch of training data.
    
    Args:
        batch: List of training data records
    """
    for record in batch:
        logger.info(f'Processing record: {record}')
        # Simulate processing logic
        time.sleep(0.1)  # Simulate delay

async def aggregate_metrics(metrics: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from processed records.
    
    Args:
        metrics: List of metrics
    Returns:
        Aggregated metrics
    """
    aggregated = {'total': len(metrics), 'success': sum(m.get('success', 0) for m in metrics)}
    return aggregated

class LLMTrainer:
    """Main orchestrator for fine-tuning LLMs."""
    def __init__(self, config: Config):
        self.config = config

    async def fine_tune(self, training_data: Dict[str, Any]) -> None:
        """Perform fine-tuning on the LLM.
        
        Args:
            training_data: Data for fine-tuning
        """
        logger.info('Starting fine-tuning process...')
        try:
            await validate_input(training_data)  # Validate input
            sanitized_data = await sanitize_fields(training_data)  # Sanitize input
            await save_to_db(sanitized_data)  # Save sanitized data
            # Simulate fine-tuning process
            await process_batch([sanitized_data])  # Process data
            logger.info('Fine-tuning completed successfully.')
        except ValueError as ve:
            logger.error(f'Validation error: {ve}')
        except Exception as e:
            logger.error(f'Error during fine-tuning: {e}')

if __name__ == '__main__':
    # Example usage
    config = Config()  # Load configuration
    trainer = LLMTrainer(config)  # Create trainer instance
    training_data = {'model_name': 'LLM-Model', 'training_data': 'data_path'}
    with resource_manager():  # Manage resources
        trainer.fine_tune(training_data)