How to Fine-tune Phi-4 for Domain-Specific Tasks: Complete Tutorial 2025

# Install required packages
pip install transformers==4.36.0
pip install torch==2.1.0
pip install datasets==2.14.0
pip install accelerate==0.24.0
pip install peft==0.6.0
pip install bitsandbytes==0.41.0

import os
import torch
from transformers import (
    AutoTokenizer, 
    AutoModelForCausalLM,
    TrainingArguments,
    Trainer
)
from peft import LoraConfig, get_peft_model
from datasets import Dataset

# Set environment variables
os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

# Check GPU availability
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

def create_domain_dataset(domain_texts, instructions, responses):
    """
    Create a structured dataset for domain-specific fine-tuning
    """
    dataset_entries = []
    
    for instruction, response in zip(instructions, responses):
        entry = {
            "instruction": instruction,
            "input": "",
            "output": response,
            "text": f"### Instruction:\n{instruction}\n\n### Response:\n{response}"
        }
        dataset_entries.append(entry)
    
    return Dataset.from_list(dataset_entries)

# Example: Medical domain dataset
medical_instructions = [
    "Explain the symptoms of Type 2 diabetes",
    "What are the contraindications for ACE inhibitors?",
    "Describe the mechanism of action of metformin"
]

medical_responses = [
    "Type 2 diabetes symptoms include frequent urination, excessive thirst, fatigue, blurred vision, and slow-healing wounds...",
    "ACE inhibitors are contraindicated in patients with bilateral renal artery stenosis, pregnancy, hyperkalemia...",
    "Metformin reduces hepatic glucose production and increases insulin sensitivity in peripheral tissues..."
]

# Create dataset
medical_dataset = create_domain_dataset([], medical_instructions, medical_responses)
print(f"Dataset size: {len(medical_dataset)}")

def preprocess_dataset(dataset, tokenizer, max_length=2048):
    """
    Preprocess dataset for Phi-4 fine-tuning
    """
    def tokenize_function(examples):
        # Tokenize the text
        tokens = tokenizer(
            examples["text"],
            truncation=True,
            padding=False,
            max_length=max_length,
            return_tensors=None
        )
        
        # Set labels for causal language modeling
        tokens["labels"] = tokens["input_ids"].copy()
        return tokens
    
    # Apply tokenization
    tokenized_dataset = dataset.map(
        tokenize_function,
        batched=True,
        remove_columns=dataset.column_names
    )
    
    return tokenized_dataset

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-mini-instruct")
tokenizer.pad_token = tokenizer.eos_token

# Preprocess the dataset
processed_dataset = preprocess_dataset(medical_dataset, tokenizer)

def load_phi4_model(model_name="microsoft/Phi-3.5-mini-instruct"):
    """
    Load Phi-4 model with optimized configuration
    """
    # Load model with 4-bit quantization for memory efficiency
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype=torch.float16,
        device_map="auto",
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.float16,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_use_double_quant=True,
        trust_remote_code=True
    )
    
    # Enable gradient checkpointing for memory efficiency
    model.gradient_checkpointing_enable()
    
    return model

# Load the model
model = load_phi4_model()
print(f"Model loaded successfully. Parameters: {model.num_parameters():,}")

def configure_lora(model, target_modules=None):
    """
    Configure LoRA for parameter-efficient fine-tuning
    """
    if target_modules is None:
        target_modules = [
            "q_proj", "k_proj", "v_proj", "o_proj",
            "gate_proj", "up_proj", "down_proj"
        ]
    
    lora_config = LoraConfig(
        r=16,  # Rank of adaptation
        lora_alpha=32,  # LoRA scaling parameter
        target_modules=target_modules,
        lora_dropout=0.1,
        bias="none",
        task_type="CAUSAL_LM"
    )
    
    # Apply LoRA to the model
    peft_model = get_peft_model(model, lora_config)
    
    # Print trainable parameters
    peft_model.print_trainable_parameters()
    
    return peft_model

# Configure LoRA
peft_model = configure_lora(model)

def create_training_args(output_dir="./phi4-domain-finetuned"):
    """
    Create optimized training arguments for Phi-4 fine-tuning
    """
    training_args = TrainingArguments(
        output_dir=output_dir,
        per_device_train_batch_size=2,
        per_device_eval_batch_size=2,
        gradient_accumulation_steps=8,
        num_train_epochs=3,
        learning_rate=2e-4,
        fp16=True,
        logging_steps=50,
        save_steps=500,
        eval_steps=500,
        evaluation_strategy="steps",
        save_strategy="steps",
        load_best_model_at_end=True,
        metric_for_best_model="eval_loss",
        greater_is_better=False,
        warmup_steps=100,
        lr_scheduler_type="cosine",
        optim="adamw_torch",
        dataloader_pin_memory=False,
        remove_unused_columns=False,
        report_to=None
    )
    
    return training_args

# Create training arguments
training_args = create_training_args()

from transformers import DataCollatorForLanguageModeling

# Create data collator for causal language modeling
data_collator = DataCollatorForLanguageModeling(
    tokenizer=tokenizer,
    mlm=False,  # Not masked language modeling
    pad_to_multiple_of=8  # Optimize for tensor cores
)

def setup_trainer(model, tokenizer, train_dataset, eval_dataset, training_args, data_collator):
    """
    Setup the Trainer for Phi-4 fine-tuning
    """
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        tokenizer=tokenizer,
        data_collator=data_collator,
    )
    
    return trainer

# Split dataset for training and evaluation
train_size = int(0.9 * len(processed_dataset))
eval_size = len(processed_dataset) - train_size

train_dataset = processed_dataset.select(range(train_size))
eval_dataset = processed_dataset.select(range(train_size, train_size + eval_size))

# Setup trainer
trainer = setup_trainer(
    peft_model, 
    tokenizer, 
    train_dataset, 
    eval_dataset, 
    training_args, 
    data_collator
)

def execute_training(trainer):
    """
    Execute the fine-tuning process with monitoring
    """
    print("Starting fine-tuning process...")
    
    # Start training
    train_result = trainer.train()
    
    # Save the final model
    trainer.save_model()
    trainer.save_state()
    
    # Print training metrics
    print(f"Training completed!")
    print(f"Final training loss: {train_result.training_loss:.4f}")
    print(f"Training time: {train_result.metrics['train_runtime']:.2f} seconds")
    
    return train_result

# Execute training
train_result = execute_training(trainer)

import matplotlib.pyplot as plt

def plot_training_metrics(trainer):
    """
    Plot training and evaluation metrics
    """
    logs = trainer.state.log_history
    
    train_losses = [log['train_loss'] for log in logs if 'train_loss' in log]
    eval_losses = [log['eval_loss'] for log in logs if 'eval_loss' in log]
    
    plt.figure(figsize=(12, 4))
    
    # Plot training loss
    plt.subplot(1, 2, 1)
    plt.plot(train_losses, label='Training Loss')
    plt.title('Training Loss Over Time')
    plt.xlabel('Steps')
    plt.ylabel('Loss')
    plt.legend()
    
    # Plot evaluation loss
    plt.subplot(1, 2, 2)
    plt.plot(eval_losses, label='Evaluation Loss', color='orange')
    plt.title('Evaluation Loss Over Time')
    plt.xlabel('Steps')
    plt.ylabel('Loss')
    plt.legend()
    
    plt.tight_layout()
    plt.show()

# Plot metrics (placeholder - actual plotting would show after training)
# plot_training_metrics(trainer)

def evaluate_model_performance(model, tokenizer, test_prompts):
    """
    Evaluate fine-tuned model performance on domain-specific tasks
    """
    model.eval()
    results = []
    
    for prompt in test_prompts:
        # Tokenize input
        inputs = tokenizer(
            prompt, 
            return_tensors="pt", 
            padding=True, 
            truncation=True
        ).to(device)
        
        # Generate response
        with torch.no_grad():
            outputs = model.generate(
                **inputs,
                max_new_tokens=200,
                temperature=0.7,
                do_sample=True,
                pad_token_id=tokenizer.eos_token_id
            )
        
        # Decode response
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        results.append({
            "prompt": prompt,
            "response": response
        })
    
    return results

# Test prompts for evaluation
test_prompts = [
    "### Instruction:\nWhat are the early signs of cardiovascular disease?\n\n### Response:\n",
    "### Instruction:\nExplain the difference between Type 1 and Type 2 diabetes.\n\n### Response:\n"
]

# Evaluate performance (placeholder)
# evaluation_results = evaluate_model_performance(peft_model, tokenizer, test_prompts)

def optimize_hyperparameters():
    """
    Guidelines for hyperparameter optimization
    """
    optimization_guide = {
        "learning_rate": {
            "range": "1e-5 to 5e-4",
            "recommendation": "Start with 2e-4 for LoRA",
            "notes": "Lower rates for stable training, higher for faster convergence"
        },
        "batch_size": {
            "range": "1 to 8 per device",
            "recommendation": "2-4 with gradient accumulation",
            "notes": "Adjust based on GPU memory availability"
        },
        "lora_rank": {
            "range": "8 to 64",
            "recommendation": "16 for most tasks",
            "notes": "Higher rank = more parameters = better performance"
        },
        "epochs": {
            "range": "1 to 10",
            "recommendation": "3-5 epochs",
            "notes": "Monitor for overfitting after 3 epochs"
        }
    }
    
    return optimization_guide

# Display optimization guide
optimization_guide = optimize_hyperparameters()
for param, details in optimization_guide.items():
    print(f"{param}: {details['recommendation']} ({details['notes']})")

def implement_advanced_techniques():
    """
    Advanced techniques for improved fine-tuning
    """
    techniques = {
        "gradient_clipping": {
            "purpose": "Prevent gradient explosion",
            "implementation": "max_grad_norm=1.0 in TrainingArguments"
        },
        "learning_rate_scheduling": {
            "purpose": "Optimize learning throughout training",
            "implementation": "lr_scheduler_type='cosine' with warmup"
        },
        "early_stopping": {
            "purpose": "Prevent overfitting",
            "implementation": "EarlyStoppingCallback with patience=3"
        },
        "mixed_precision": {
            "purpose": "Reduce memory usage and speed up training",
            "implementation": "fp16=True in TrainingArguments"
        }
    }
    
    return techniques

# Display advanced techniques
advanced_techniques = implement_advanced_techniques()
for technique, details in advanced_techniques.items():
    print(f"{technique}: {details['purpose']}")

def calculate_domain_metrics(predictions, references):
    """
    Calculate domain-specific evaluation metrics
    """
    from sklearn.metrics import accuracy_score, f1_score
    import numpy as np
    
    # Example metrics for classification tasks
    metrics = {
        "accuracy": accuracy_score(references, predictions),
        "f1_score": f1_score(references, predictions, average='weighted'),
        "domain_accuracy": calculate_domain_accuracy(predictions, references)
    }
    
    return metrics

def calculate_domain_accuracy(predictions, references):
    """
    Calculate accuracy for domain-specific terminology
    """
    # Custom logic for domain-specific evaluation
    correct_domain_terms = 0
    total_domain_terms = 0
    
    # Implementation depends on your specific domain
    # This is a placeholder for domain-specific accuracy calculation
    
    return correct_domain_terms / total_domain_terms if total_domain_terms > 0 else 0

# Placeholder for actual evaluation
print("Evaluation metrics will be calculated based on your specific domain requirements")

def compare_models(original_model, fine_tuned_model, test_cases):
    """
    Compare performance between original and fine-tuned models
    """
    comparison_results = []
    
    for test_case in test_cases:
        # Get responses from both models
        original_response = generate_response(original_model, test_case)
        fine_tuned_response = generate_response(fine_tuned_model, test_case)
        
        comparison_results.append({
            "test_case": test_case,
            "original_response": original_response,
            "fine_tuned_response": fine_tuned_response,
            "improvement_score": calculate_improvement_score(
                original_response, 
                fine_tuned_response
            )
        })
    
    return comparison_results

def generate_response(model, prompt):
    """
    Generate response from model for comparison
    """
    # Implementation for response generation
    return "Generated response placeholder"

def calculate_improvement_score(original, fine_tuned):
    """
    Calculate improvement score between responses
    """
    # Custom scoring logic based on domain requirements
    return 0.85  # Placeholder score

def prepare_model_for_deployment(model_path):
    """
    Prepare fine-tuned model for production deployment
    """
    # Load the fine-tuned model
    model = AutoModelForCausalLM.from_pretrained(
        model_path,
        torch_dtype=torch.float16,
        device_map="auto"
    )
    
    # Optimize for inference
    model.eval()
    
    # Enable optimizations
    model = torch.compile(model)  # PyTorch 2.0 optimization
    
    return model

def create_inference_pipeline(model, tokenizer):
    """
    Create optimized inference pipeline
    """
    from transformers import pipeline
    
    # Create text generation pipeline
    pipe = pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
        torch_dtype=torch.float16,
        device_map="auto"
    )
    
    return pipe

# Deployment preparation
deployment_model_path = "./phi4-domain-finetuned"
# deployed_model = prepare_model_for_deployment(deployment_model_path)
# inference_pipeline = create_inference_pipeline(deployed_model, tokenizer)

from flask import Flask, request, jsonify

def create_api_server(inference_pipeline):
    """
    Create Flask API server for model serving
    """
    app = Flask(__name__)
    
    @app.route('/generate', methods=['POST'])
    def generate_text():
        try:
            # Get input from request
            data = request.json
            prompt = data.get('prompt', '')
            max_length = data.get('max_length', 200)
            
            # Generate response
            result = inference_pipeline(
                prompt,
                max_new_tokens=max_length,
                temperature=0.7,
                do_sample=True,
                return_full_text=False
            )
            
            return jsonify({
                'success': True,
                'response': result[0]['generated_text']
            })
            
        except Exception as e:
            return jsonify({
                'success': False,
                'error': str(e)
            }), 500
    
    @app.route('/health', methods=['GET'])
    def health_check():
        return jsonify({'status': 'healthy'})
    
    return app

# API server setup (placeholder)
# app = create_api_server(inference_pipeline)
# app.run(host='0.0.0.0', port=5000)

def optimize_inference_performance():
    """
    Techniques for optimizing inference performance
    """
    optimization_strategies = {
        "quantization": {
            "description": "Reduce model precision for faster inference",
            "implementation": "Use 8-bit or 4-bit quantization",
            "performance_gain": "2-4x speedup"
        },
        "caching": {
            "description": "Cache frequent responses",
            "implementation": "Redis or in-memory caching",
            "performance_gain": "10-100x for repeated queries"
        },
        "batching": {
            "description": "Process multiple requests together",
            "implementation": "Dynamic batching with timeout",
            "performance_gain": "2-5x throughput improvement"
        },
        "model_compilation": {
            "description": "Compile model for target hardware",
            "implementation": "torch.compile() or TensorRT",
            "performance_gain": "1.5-3x speedup"
        }
    }
    
    return optimization_strategies

# Display optimization strategies
optimization_strategies = optimize_inference_performance()
for strategy, details in optimization_strategies.items():
    print(f"{strategy}: {details['performance_gain']}")

import logging
from datetime import datetime

def setup_production_monitoring():
    """
    Setup monitoring and logging for production deployment
    """
    # Configure logging
    logging.basicConfig(
        level=logging.INFO,
        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
        handlers=[
            logging.FileHandler('phi4_inference.log'),
            logging.StreamHandler()
        ]
    )
    
    logger = logging.getLogger(__name__)
    
    def log_inference_metrics(prompt, response, inference_time):
        """
        Log inference metrics for monitoring
        """
        metrics = {
            "timestamp": datetime.now().isoformat(),
            "prompt_length": len(prompt),
            "response_length": len(response),
            "inference_time_ms": inference_time * 1000,
            "tokens_per_second": len(response.split()) / inference_time
        }
        
        logger.info(f"Inference metrics: {metrics}")
        return metrics
    
    return logger, log_inference_metrics

# Setup monitoring
logger, log_metrics = setup_production_monitoring()

def resolve_memory_issues():
    """
    Common memory issues and solutions
    """
    solutions = {
        "out_of_memory": {
            "symptoms": "CUDA out of memory error during training",
            "solutions": [
                "Reduce batch size to 1",
                "Enable gradient checkpointing",
                "Use 4-bit quantization",
                "Increase gradient accumulation steps"
            ]
        },
        "slow_training": {
            "symptoms": "Training takes too long",
            "solutions": [
                "Use mixed precision training (fp16)",
                "Optimize data loading with num_workers",
                "Use gradient accumulation",
                "Enable torch.compile()"
            ]
        },
        "memory_leaks": {
            "symptoms": "Memory usage increases over time",
            "solutions": [
                "Clear cache regularly with torch.cuda.empty_cache()",
                "Use context managers for inference",
                "Avoid keeping references to tensors",
                "Use del to remove unused variables"
            ]
        }
    }
    
    return solutions

# Display troubleshooting guide
memory_solutions = resolve_memory_issues()
for issue, details in memory_solutions.items():
    print(f"{issue}: {len(details['solutions'])} solutions available")

def debug_training_issues():
    """
    Debug common training convergence problems
    """
    debugging_checklist = {
        "loss_not_decreasing": [
            "Check learning rate (try 1e-5 to 5e-4)",
            "Verify dataset quality and formatting",
            "Ensure proper tokenization",
            "Check for gradient clipping issues"
        ],
        "overfitting": [
            "Reduce number of epochs",
            "Increase dropout rate",
            "Use more diverse training data",
            "Implement early stopping"
        ],
        "unstable_training": [
            "Lower learning rate",
            "Add gradient clipping",
            "Use warmup steps",
            "Check for data corruption"
        ],
        "poor_domain_performance": [
            "Increase domain-specific data",
            "Verify data quality and accuracy",
            "Adjust LoRA rank and alpha",
            "Check tokenizer compatibility"
        ]
    }
    
    return debugging_checklist

# Display debugging checklist
debugging_guide = debug_training_issues()
for problem, solutions in debugging_guide.items():
    print(f"{problem}: {len(solutions)} debugging steps")

def data_best_practices():
    """
    Best practices for domain-specific data preparation
    """
    practices = {
        "data_quality": {
            "requirements": [
                "Minimum 1000 high-quality examples",
                "Consistent formatting across all samples",
                "Expert-reviewed domain content",
                "Balanced representation of domain topics"
            ]
        },
        "data_diversity": {
            "requirements": [
                "Cover various domain subtopics",
                "Include different question types",
                "Mix simple and complex examples",
                "Represent different writing styles"
            ]
        },
        "data_preprocessing": {
            "requirements": [
                "Remove duplicates and near-duplicates",
                "Normalize text formatting",
                "Handle special characters properly",
                "Validate all domain-specific terms"
            ]
        }
    }
    
    return practices

# Display best practices
best_practices = data_best_practices()
for category, details in best_practices.items():
    print(f"{category}: {len(details['requirements'])} requirements")

def training_recommendations():
    """
    Recommended training configurations for different scenarios
    """
    configurations = {
        "small_dataset": {
            "data_size": "< 5000 examples",
            "config": {
                "epochs": 5,
                "learning_rate": 1e-4,
                "lora_rank": 8,
                "batch_size": 2
            }
        },
        "medium_dataset": {
            "data_size": "5000-20000 examples",
            "config": {
                "epochs": 3,
                "learning_rate": 2e-4,
                "lora_rank": 16,
                "batch_size": 4
            }
        },
        "large_dataset": {
            "data_size": "> 20000 examples",
            "config": {
                "epochs": 2,
                "learning_rate": 3e-4,
                "lora_rank": 32,
                "batch_size": 8
            }
        }
    }
    
    return configurations

# Display training recommendations
training_configs = training_recommendations()
for scenario, details in training_configs.items():
    print(f"{scenario}: {details['data_size']} - LR: {details['config']['learning_rate']}")

def medical_domain_example():
    """
    Example implementation for medical domain fine-tuning
    """
    medical_config = {
        "domain": "Healthcare/Medical",
        "use_cases": [
            "Medical Q&A systems",
            "Clinical decision support",
            "Medical literature summarization",
            "Patient education materials"
        ],
        "data_sources": [
            "Medical textbooks",
            "Clinical guidelines",
            "Research papers",
            "Medical Q&A databases"
        ],
        "evaluation_metrics": [
            "Medical accuracy",
            "Safety compliance",
            "Terminology correctness",
            "Clinical relevance"
        ]
    }
    
    return medical_config

# Display medical domain example
medical_example = medical_domain_example()
print(f"Medical domain: {len(medical_example['use_cases'])} use cases")

def legal_domain_example():
    """
    Example implementation for legal domain fine-tuning
    """
    legal_config = {
        "domain": "Legal/Law",
        "use_cases": [
            "Legal document analysis",
            "Contract review assistance",
            "Legal research support",
            "Compliance checking"
        ],
        "data_sources": [
            "Legal cases and precedents",
            "Statutory texts",
            "Legal commentary",
            "Bar exam questions"
        ],
        "special_considerations": [
            "Jurisdiction-specific training",
            "Regular updates for law changes",
            "Ethical guidelines compliance",
            "Professional liability considerations"
        ]
    }
    
    return legal_config

# Display legal domain example
legal_example = legal_domain_example()
print(f"Legal domain: {len(legal_example['use_cases'])} use cases")

def financial_domain_example():
    """
    Example implementation for financial domain fine-tuning
    """
    financial_config = {
        "domain": "Finance/Banking",
        "use_cases": [
            "Investment advice generation",
            "Financial report analysis",
            "Risk assessment support",
            "Regulatory compliance checking"
        ],
        "data_sources": [
            "Financial statements",
            "Market analysis reports",
            "Regulatory documents",
            "Investment research"
        ],
        "performance_metrics": [
            "Financial accuracy",
            "Market terminology usage",
            "Regulatory compliance",
            "Risk assessment quality"
        ]
    }
    
    return financial_config

# Display financial domain example
financial_example = financial_domain_example()
print(f"Financial domain: {len(financial_example['use_cases'])} use cases")

def implement_multi_task_learning():
    """
    Implement multi-task learning for domain expertise
    """
    multi_task_config = {
        "approach": "Train on multiple related tasks simultaneously",
        "benefits": [
            "Better generalization within domain",
            "Improved knowledge transfer",
            "More robust performance",
            "Efficient parameter usage"
        ],
        "implementation": {
            "task_weighting": "Balance loss functions for different tasks",
            "shared_parameters": "Use common backbone with task-specific heads",
            "curriculum_learning": "Start with easier tasks, progress to complex ones"
        }
    }
    
    return multi_task_config

# Display multi-task learning approach
multi_task_approach = implement_multi_task_learning()
print(f"Multi-task learning: {len(multi_task_approach['benefits'])} key benefits")

def domain_adaptation_strategies():
    """
    Advanced domain adaptation techniques
    """
    strategies = {
        "progressive_fine_tuning": {
            "description": "Gradually adapt from general to specific domain",
            "steps": [
                "Start with general domain data",
                "Introduce domain-specific vocabulary",
                "Fine-tune on target domain tasks",
                "Optimize for domain-specific metrics"
            ]
        },
        "adversarial_training": {
            "description": "Use adversarial examples to improve robustness",
            "benefits": [
                "Better handling of edge cases",
                "Improved generalization",
                "Reduced overfitting to training data"
            ]
        },
        "knowledge_distillation": {
            "description": "Transfer knowledge from larger models",
            "process": [
                "Train large teacher model on domain data",
                "Extract knowledge through soft targets",
                "Train Phi-4 student model to match teacher",
                "Optimize for both accuracy and efficiency"
            ]
        }
    }
    
    return strategies

# Display domain adaptation strategies
adaptation_strategies = domain_adaptation_strategies()
for strategy, details in adaptation_strategies.items():
    print(f"{strategy}: {details['description']}")

def create_domain_benchmark():
    """
    Create comprehensive benchmark for domain-specific evaluation
    """
    benchmark_framework = {
        "accuracy_tests": {
            "domain_knowledge": "Test understanding of domain concepts",
            "terminology_usage": "Evaluate proper use of domain terms",
            "factual_correctness": "Verify accuracy of domain facts"
        },
        "capability_tests": {
            "reasoning": "Test logical reasoning within domain",
            "problem_solving": "Evaluate complex problem-solving abilities",
            "synthesis": "Test ability to combine domain knowledge"
        },
        "robustness_tests": {
            "edge_cases": "Test handling of unusual scenarios",
            "ambiguity": "Evaluate response to ambiguous queries",
            "contradictions": "Test handling of conflicting information"
        },
        "safety_tests": {
            "harmful_content": "Ensure no generation of harmful advice",
            "bias_detection": "Test for unfair bias in responses",
            "ethical_compliance": "Verify ethical standards adherence"
        }
    }
    
    return benchmark_framework

# Display benchmark framework
benchmark_framework = create_domain_benchmark()
for category, tests in benchmark_framework.items():
    print(f"{category}: {len(tests)} test types")

def create_evaluation_pipeline():
    """
    Create automated evaluation pipeline for continuous monitoring
    """
    pipeline_config = {
        "evaluation_frequency": "Weekly automated runs",
        "test_categories": [
            "Regression testing on core capabilities",
            "Performance monitoring on new data",
            "Bias and safety evaluations",
            "User satisfaction metrics"
        ],
        "reporting": {
            "metrics_dashboard": "Real-time performance visualization",
            "alert_system": "Notifications for performance degradation",
            "trend_analysis": "Long-term performance tracking",
            "improvement_suggestions": "Automated recommendations"
        }
    }
    
    return pipeline_config

# Display evaluation pipeline
eval_pipeline = create_evaluation_pipeline()
print(f"Evaluation pipeline: {len(eval_pipeline['test_categories'])} categories")

def analyze_fine_tuning_costs():
    """
    Analyze and optimize fine-tuning costs
    """
    cost_breakdown = {
        "compute_costs": {
            "training": "GPU hours for initial fine-tuning",
            "inference": "Ongoing serving costs",
            "storage": "Model and data storage costs",
            "bandwidth": "Data transfer and API costs"
        },
        "optimization_strategies": {
            "efficient_training": [
                "Use parameter-efficient methods (LoRA)",
                "Implement gradient accumulation",
                "Optimize batch sizes",
                "Use mixed precision training"
            ],
            "inference_optimization": [
                "Model quantization",
                "Caching strategies",
                "Batch inference",
                "Auto-scaling deployment"
            ]
        },
        "cost_monitoring": {
            "tracking_metrics": [
                "Cost per training hour",
                "Cost per inference request",
                "Resource utilization rates",
                "Performance per dollar"
            ]
        }
    }
    
    return cost_breakdown

# Display cost analysis
cost_analysis = analyze_fine_tuning_costs()
print(f"Cost optimization: {len(cost_analysis['optimization_strategies']['efficient_training'])} training strategies")

def implement_resource_scaling():
    """
    Implement dynamic resource scaling for cost efficiency
    """
    scaling_strategies = {
        "auto_scaling": {
            "triggers": [
                "Request volume thresholds",
                "Response latency targets",
                "Resource utilization limits",
                "Cost budget constraints"
            ],
            "actions": [
                "Scale up/down compute instances",
                "Adjust model serving replicas",
                "Optimize memory allocation",
                "Switch between model variants"
            ]
        },
        "cost_controls": {
            "budgets": "Set spending limits per time period",
            "alerts": "Notify when approaching budget limits",
            "auto_shutdown": "Stop resources when idle",
            "optimization_suggestions": "Recommend cost-saving changes"
        }
    }
    
    return scaling_strategies

# Display scaling strategies
scaling_config = implement_resource_scaling()
print(f"Auto-scaling: {len(scaling_config['auto_scaling']['triggers'])} triggers configured")

def implement_model_versioning():
    """
    Implement comprehensive model versioning system
    """
    versioning_system = {
        "version_control": {
            "semantic_versioning": "Major.Minor.Patch format",
            "change_tracking": "Log all modifications and improvements",
            "rollback_capability": "Quick revert to previous versions",
            "A/B_testing": "Compare model versions in production"
        },
        "update_strategies": {
            "incremental_updates": "Regular small improvements",
            "major_retraining": "Periodic complete retraining",
            "emergency_patches": "Quick fixes for critical issues",
            "scheduled_maintenance": "Planned update windows"
        },
        "deployment_pipeline": {
            "staging_environment": "Test updates before production",
            "gradual_rollout": "Incremental traffic switching",
            "monitoring": "Track performance during updates",
            "automatic_rollback": "Revert if issues detected"
        }
    }
    
    return versioning_system

# Display versioning system
versioning_config = implement_model_versioning()
print(f"Versioning system: {len(versioning_config['version_control'])} components")

def setup_continuous_learning():
    """
    Setup framework for continuous model improvement
    """
    learning_framework = {
        "data_collection": {
            "user_feedback": "Collect ratings and corrections",
            "interaction_logs": "Track user queries and responses",
            "domain_updates": "Monitor field developments",
            "performance_metrics": "Continuous evaluation results"
        },
        "learning_triggers": {
            "performance_degradation": "Retrain when accuracy drops",
            "new_domain_knowledge": "Update with latest information",
            "user_pattern_changes": "Adapt to changing user needs",
            "scheduled_updates": "Regular improvement cycles"
        },
        "learning_methods": {
            "online_learning": "Real-time adaptation to new data",
            "batch_updates": "Periodic retraining on accumulated data",
            "transfer_learning": "Leverage improvements from related domains",
            "ensemble_methods": "Combine multiple model versions"
        }
    }
    
    return learning_framework

# Display continuous learning framework
learning_config = setup_continuous_learning()
print(f"Continuous learning: {len(learning_config['learning_methods'])} methods available")