Align Industrial LLMs with RLHF and Hugging Face TRL for Manufacturing Use Cases

                      
                     
from typing import Dict, Any
import os
import logging
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import RewardTrainer

# Configuration
class Config:
    API_KEY: str = os.getenv('API_KEY')
    MODEL_NAME: str = 'gpt-2'
    LOG_LEVEL: str = os.getenv('LOG_LEVEL', 'INFO')

# Initialize logging
logging.basicConfig(level=Config.LOG_LEVEL)
logger = logging.getLogger(__name__)

# Load model and tokenizer
try:
    tokenizer = AutoTokenizer.from_pretrained(Config.MODEL_NAME)
    model = AutoModelForCausalLM.from_pretrained(Config.MODEL_NAME)
    logger.info('Model and tokenizer loaded successfully.')
except Exception as e:
    logger.error(f'Failed to load model: {str(e)}')
    raise

# Initialize Reward Trainer
reward_trainer = RewardTrainer(model=model)

# Core logic for RLHF
async def align_model_with_feedback(feedback: Dict[str, Any]) -> Dict[str, Any]:
    try:
        # Process feedback
        response = model.generate(tokenizer.encode(feedback['input'], return_tensors='pt'))
        reward = feedback['reward']
        reward_trainer.update(response, reward)
        return {'success': True, 'response': response}
    except Exception as e:
        logger.error(f'Error during feedback processing: {str(e)}')
        return {'success': False, 'error': str(e)}

if __name__ == '__main__':
    # Sample feedback for demonstration
    sample_feedback = {'input': 'What is AI?', 'reward': 1.0}
    result = align_model_with_feedback(sample_feedback)
    print(result)