小規模言語モデル（SLM）がエージェント型AIの未来である理由

class MetaGPTAgent:
    def __init__(self, role, specialized_slm):
        self.role = role
        self.slm = specialized_slm  # 役割特化SLM
        self.fallback_llm = None   # 複雑タスク用LLM
    
    def execute_task(self, task):
        if self.is_routine_task(task):
            return self.slm.generate(task)
        else:
            return self.fallback_llm.generate(task)

class InferenceScheduler:
    def __init__(self):
        self.slm_pool = SLMPool()
        self.llm_pool = LLMPool()
    
    def route_request(self, request):
        complexity_score = self.analyze_complexity(request)
        
        if complexity_score < THRESHOLD:
            return self.slm_pool.get_available_model(request.task_type)
        else:
            return self.llm_pool.get_model()

def dynamic_load_balancing(request_queue):
    for request in request_queue:
        # リクエストの特性分析
        task_characteristics = analyze_request(request)
        
        # 最適モデルの選択
        if task_characteristics['repetitive'] and task_characteristics['structured']:
            assign_to_specialized_slm(request)
        elif task_characteristics['complex_reasoning']:
            assign_to_llm(request)
        else:
            assign_to_general_slm(request)

def assess_current_llm_usage():
    usage_patterns = analyze_llm_call_logs()
    
    # タスクの複雑さ分析
    task_complexity = categorize_tasks(usage_patterns)
    
    # SLM適用可能性評価
    slm_candidates = identify_slm_opportunities(task_complexity)
    
    return create_migration_plan(slm_candidates)

def pilot_slm_implementation(selected_tasks):
    for task in selected_tasks:
        # 専用SLMの訓練
        specialized_slm = train_task_specific_slm(task)
        
        # A/Bテスト実施
        performance_comparison = run_ab_test(
            llm_baseline=current_llm,
            slm_candidate=specialized_slm,
            test_dataset=task.test_data
        )
        
        if performance_comparison.slm_meets_criteria():
            deploy_slm_for_task(task, specialized_slm)

class HybridAgentSystem:
    def __init__(self):
        self.task_router = TaskRouter()
        self.slm_models = {}  # タスク特化SLM
        self.fallback_llm = GeneralLLM()
    
    def process_request(self, request):
        task_type = self.task_router.classify(request)
        
        if task_type in self.slm_models:
            try:
                return self.slm_models[task_type].process(request)
            except Exception:
                return self.fallback_llm.process(request)
        else:
            return self.fallback_llm.process(request)

class QualityMonitor:
    def __init__(self):
        self.metrics = ['accuracy', 'latency', 'cost', 'user_satisfaction']
    
    def monitor_slm_performance(self, slm_outputs):
        for metric in self.metrics:
            current_value = calculate_metric(metric, slm_outputs)
            baseline_value = get_baseline_value(metric)
            
            if current_value < baseline_value * THRESHOLD:
                trigger_retraining_alert(metric, current_value)
    
    def adaptive_improvement(self):
        # 性能低下の検出と自動改善
        underperforming_models = identify_underperforming_slms()
        
        for model in underperforming_models:
            retrain_with_recent_data(model)

# 70億パラメータSLM vs 700億パラメータLLM
推論速度: 10-30倍高速
エネルギー消費: 10-20倍削減
インフラコスト: 5-15倍削減
総運用コスト: 15-40倍削減

def calculate_roi(current_llm_costs, slm_migration_costs, efficiency_gains):
    # 初期移行コスト
    migration_investment = slm_migration_costs['training'] + slm_migration_costs['infrastructure']
    
    # 年間節約額
    annual_savings = current_llm_costs * efficiency_gains['cost_reduction_ratio']
    
    # ROI計算
    payback_period = migration_investment / annual_savings
    five_year_roi = (annual_savings * 5 - migration_investment) / migration_investment
    
    return {
        'payback_period_months': payback_period * 12,
        'five_year_roi_percentage': five_year_roi * 100
    }

class EdgeSLMDeployment:
    def __init__(self):
        self.edge_devices = discover_edge_resources()
        self.model_optimizer = ModelOptimizer()
    
    def deploy_to_edge(self, slm_model, target_device):
        # デバイス特性に応じた最適化
        optimized_model = self.model_optimizer.optimize_for_device(
            model=slm_model,
            device_specs=target_device.specs
        )
        
        # エッジ展開
        deployment_config = create_edge_deployment_config(optimized_model)
        return deploy_model(optimized_model, target_device, deployment_config)

def federated_slm_training(distributed_agents):
    """分散エージェントからの連合学習"""
    global_model = initialize_global_slm()
    
    for round in training_rounds:
        local_updates = []
        
        for agent in distributed_agents:
            # ローカル訓練
            local_model = train_locally(global_model, agent.local_data)
            local_updates.append(extract_updates(local_model))
        
        # グローバルモデル更新
        global_model = aggregate_updates(global_model, local_updates)
    
    return global_model

class HierarchicalAgentSystem:
    def __init__(self):
        self.coordinator_llm = CoordinatorLLM()      # 高レベル計画
        self.specialist_slms = {                     # 専門実行
            'code_generation': CodeGenerationSLM(),
            'data_processing': DataProcessingSLM(),
            'interface_control': InterfaceControlSLM()
        }
    
    def execute_complex_task(self, task):
        # 高レベル分解
        subtasks = self.coordinator_llm.decompose_task(task)
        
        # 専門SLMによる実行
        results = []
        for subtask in subtasks:
            specialist = self.get_specialist(subtask.type)
            result = specialist.execute(subtask)
            results.append(result)
        
        # 結果統合
        return self.coordinator_llm.integrate_results(results)

# 実践的なスキル習得例
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig, get_peft_model

# 小規模モデルでの実験環境構築
model_name = "microsoft/DialoGPT-small"  # 練習用小規模モデル
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# LoRA適用の練習
lora_config = LoraConfig(r=16, lora_alpha=32, target_modules=["c_attn"])
model = get_peft_model(model, lora_config)