# Production-ready LoRA implementation with PyTorch and Hugging Face
import torch
from transformers import AutoModelForCausalLM, TrainingArguments
from peft import LoraConfig, get_peft_model, TaskType
import logging
from dataclasses import dataclass
from typing import Optional

@dataclass
class LoraTrainingConfig:
    """Centralized configuration for LoRA fine-tuning"""
    r: int = 8  # Rank of low-rank matrices
    lora_alpha: int = 32  # Scaling factor
    target_modules: list = None  # Auto-detected if None
    lora_dropout: float = 0.1
    bias: str = "none"  # LoRA bias type

    def __post_init__(self):
        if self.target_modules is None:
            # Default to attention layers for transformer models
            self.target_modules = ["q_proj", "v_proj", "k_proj", "o_proj"]

class EnterpriseFineTuner:
    """Production-grade fine-tuning orchestrator with monitoring"""

    def __init__(self, base_model_name: str, config: LoraTrainingConfig):
        self.logger = self._setup_logging()
        self.config = config
        self.base_model = self._load_model(base_model_name)
        self.peft_model = self._apply_lora()

    def _setup_logging(self) -> logging.Logger:
        """Configure structured logging for training observability"""
        logger = logging.getLogger(__name__)
        handler = logging.StreamHandler()
        formatter = logging.Formatter(
            '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        )
        handler.setFormatter(formatter)
        logger.addHandler(handler)
        logger.setLevel(logging.INFO)
        return logger

    def _load_model(self, model_name: str):
        """Load model with memory optimization for production environments"""
        try:
            # Use 4-bit quantization for memory efficiency
            model = AutoModelForCausalLM.from_pretrained(
                model_name,
                load_in_4bit=True,  # QLoRA technique
                device_map="auto",
                torch_dtype=torch.float16
            )
            self.logger.info(f"Successfully loaded {model_name} with 4-bit quantization")
            return model
        except Exception as e:
            self.logger.error(f"Model loading failed: {str(e)}")
            raise

    def _apply_lora(self):
        """Apply LoRA configuration with validation"""
        peft_config = LoraConfig(
            task_type=TaskType.CAUSAL_LM,
            inference_mode=False,
            r=self.config.r,
            lora_alpha=self.config.lora_alpha,
            lora_dropout=self.config.lora_dropout,
            target_modules=self.config.target_modules,
            bias=self.config.bias
        )

        model = get_peft_model(self.base_model, peft_config)
        trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
        total_params = sum(p.numel() for p in model.parameters())

        self.logger.info(
            f"LoRA applied: {trainable_params:,} trainable parameters "
            f"({trainable_params/total_params:.2%} of total)"
        )
        return model

    def train(self, dataset, training_args: TrainingArguments):
        """Execute training with checkpointing and monitoring"""
        # Implementation continues with training loop
        pass

python
# Data validation and preprocessing pipeline
import pandas as pd
from sklearn.model_selection import train_test_split
import json
from typing import Dict, List, Tuple
import hashlib

class DataQualityPipeline:
    """Ensures training data meets quality standards"""

    def __init__(self, min_samples: int = 1000, max_length: int = 2048):
        self.min_samples = min_samples
        self.max_length = max_length
        self.quality_metrics = {}

    def validate_dataset(self, data_path: str) -> Dict:
        """Comprehensive data validation with quality scoring"""
        with open(data_path, 'r') as f:
            dataset = [json.loads(line) for line in f]

        validation_results = {
            "total_samples": len(dataset),
            "length_distribution": self._analyze_lengths(dataset),
            "duplicate_rate": self._check_duplicates(dataset),
            "label_distribution": self._analyze_labels(dataset),
            "format_errors": self._validate_format(dataset)
        }

        quality_score = self._calculate_quality_score(validation_results)
        validation_results["quality_score"] = quality_score

        if quality_score <

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