Comprehensive comparison for Fine-tuning technology in AI applications

# Fine-tuning a Large Language Model using Axolotl
# Production-ready configuration for training a customer support chatbot

import yaml
import os
import torch
from pathlib import Path
from axolotl.cli import load_datasets, load_cfg
from axolotl.common.cli import TrainerCliArgs
from transformers import AutoTokenizer, AutoModelForCausalLM

def create_axolotl_config():
    """
    Creates a production-ready Axolotl configuration for fine-tuning.
    Optimized for customer support use case with QLoRA.
    """
    config = {
        "base_model": "mistralai/Mistral-7B-v0.1",
        "model_type": "MistralForCausalLM",
        "tokenizer_type": "LlamaTokenizer",
        
        # Dataset configuration
        "datasets": [
            {
                "path": "data/customer_support_train.jsonl",
                "type": "alpaca",
                "ds_type": "json"
            }
        ],
        
        # QLoRA configuration for efficient training
        "adapter": "qlora",
        "lora_r": 32,
        "lora_alpha": 16,
        "lora_dropout": 0.05,
        "lora_target_modules": ["q_proj", "v_proj", "k_proj", "o_proj"],
        
        # Training hyperparameters
        "sequence_len": 2048,
        "sample_packing": True,
        "pad_to_sequence_len": True,
        
        "micro_batch_size": 2,
        "gradient_accumulation_steps": 4,
        "num_epochs": 3,
        "learning_rate": 0.0002,
        "lr_scheduler": "cosine",
        "warmup_steps": 100,
        
        # Optimization settings
        "optimizer": "adamw_torch",
        "weight_decay": 0.01,
        "gradient_checkpointing": True,
        "bf16": True,
        "tf32": True,
        
        # Evaluation and logging
        "val_set_size": 0.1,
        "eval_steps": 50,
        "save_steps": 100,
        "logging_steps": 10,
        "output_dir": "./outputs/customer-support-model",
        
        # Safety and stability
        "max_grad_norm": 1.0,
        "early_stopping_patience": 3
    }
    
    return config

def validate_environment():
    """Validates the training environment and dependencies."""
    try:
        assert torch.cuda.is_available(), "CUDA not available"
        assert torch.cuda.device_count() > 0, "No GPU devices found"
        print(f"✓ Environment validated: {torch.cuda.device_count()} GPU(s) available")
        return True
    except AssertionError as e:
        print(f"✗ Environment validation failed: {e}")
        return False

def prepare_training_data(data_path: str):
    """Validates and prepares training data in Alpaca format."""
    if not os.path.exists(data_path):
        raise FileNotFoundError(f"Training data not found at {data_path}")
    
    # Ensure data directory exists
    Path(data_path).parent.mkdir(parents=True, exist_ok=True)
    print(f"✓ Training data validated at {data_path}")

def run_training():
    """Main training function with error handling."""
    try:
        # Validate environment
        if not validate_environment():
            raise RuntimeError("Environment validation failed")
        
        # Create and save config
        config = create_axolotl_config()
        config_path = "config.yml"
        
        with open(config_path, 'w') as f:
            yaml.dump(config, f, default_flow_style=False)
        
        print(f"✓ Configuration saved to {config_path}")
        
        # Validate training data
        prepare_training_data(config['datasets'][0]['path'])
        
        # Load configuration using Axolotl
        cfg = load_cfg(config_path)
        
        # Initialize training
        print("Starting model fine-tuning...")
        print(f"Base model: {cfg.base_model}")
        print(f"Output directory: {cfg.output_dir}")
        
        # Note: Actual training would be initiated via Axolotl CLI
        # axolotl train config.yml
        
        return True
        
    except FileNotFoundError as e:
        print(f"✗ File error: {e}")
        return False
    except RuntimeError as e:
        print(f"✗ Runtime error: {e}")
        return False
    except Exception as e:
        print(f"✗ Unexpected error: {e}")
        return False

if __name__ == "__main__":
    success = run_training()
    exit(0 if success else 1)