Fine-Tune Quantized LLMs on Industrial Data with bitsandbytes and TRL

                      
                     
"""
Production implementation for Fine-Tuning Quantized LLMs on Industrial Data with bitsandbytes and TRL.
Provides secure, scalable operations tailored for industrial applications.
"""

from typing import Dict, Any, List
import os
import logging
import time
from bitsandbytes import quantize
from transformers import AutoModelForCausalLM, Trainer, TrainingArguments
import numpy as np

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

class Config:
    """
    Configuration class to manage environment variables.
    """
    model_name: str = os.getenv('MODEL_NAME', 'gpt2')  # Default model
    data_source: str = os.getenv('DATA_SOURCE', 'data.json')
    output_dir: str = os.getenv('OUTPUT_DIR', './output')

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input data to validate
    Returns:
        True if the data is valid
    Raises:
        ValueError: If validation fails
    """
    if 'id' not in data:
        raise ValueError('Missing id in input data.')  # Ensure 'id' is present
    return True

async def fetch_data(file_path: str) -> List[Dict[str, Any]]:
    """Fetch data from a specified source.
    
    Args:
        file_path: Path to the data source
    Returns:
        List of data records
    Raises:
        FileNotFoundError: If the file does not exist
    """
    if not os.path.exists(file_path):
        raise FileNotFoundError(f'The file {file_path} does not exist.')  # Check if file exists
    logger.info(f'Fetching data from {file_path}.')  # Log fetching action
    # Simulated data fetching logic
    return [{'id': 1, 'text': 'Sample data for training...'}]  # Placeholder data

async def normalize_data(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Normalize input data for processing.
    
    Args:
        data: List of input data records
    Returns:
        Normalized data records
    """
    logger.info('Normalizing data.')  # Log normalization action
    return [{'id': record['id'], 'text': record['text'].lower()} for record in data]  # Normalize text to lower case

async def quantize_model(model: Any) -> Any:
    """Quantize the model for efficient inference.
    
    Args:
        model: The model to quantize
    Returns:
        Quantized model
    """
    logger.info('Quantizing model.')  # Log quantization action
    return quantize(model)  # Quantize the model using bitsandbytes

async def process_batch(data: List[Dict[str, Any]], model: Any) -> None:
    """Process a batch of data through the model.
    
    Args:
        data: List of normalized data
        model: The model to use for processing
    """
    logger.info('Processing batch of data.')  # Log batch processing
    for record in data:
        # Simulate processing
        output = model(record['text'])  # Placeholder for model inference
        logger.info(f'Processed record ID {record["id"]} with output: {output}')  # Log output

async def save_model(model: Any, output_dir: str) -> None:
    """Save the fine-tuned model to the specified directory.
    
    Args:
        model: The model to save
        output_dir: Directory where the model will be saved
    """
    logger.info(f'Saving model to {output_dir}.')  # Log saving action
    model.save_pretrained(output_dir)  # Save model using Hugging Face method

async def main():
    """Main orchestration function to run the fine-tuning process.
    """
    logger.info('Starting fine-tuning process.')  # Log start of the process
    config = Config()  # Load configuration
    raw_data = await fetch_data(config.data_source)  # Fetch data
    validated_data = await normalize_data(raw_data)  # Normalize data
    model = AutoModelForCausalLM.from_pretrained(config.model_name)  # Load model
    quantized_model = await quantize_model(model)  # Quantize loaded model
    await process_batch(validated_data, quantized_model)  # Process the data
    await save_model(quantized_model, config.output_dir)  # Save the model

if __name__ == '__main__':
    import asyncio
    asyncio.run(main())  # Run the async main function