FineTuneX：2025年AI模型微调框架的技术突破与实践

# 模型选择层核心代码示例
class ModelSelector:
    def __init__(self):
        self.model_repository = ModelRepository()
        self.model_loader = ModelLoader()
        self.model_analyzer = ModelAnalyzer()
    
    def get_model(self, model_name, model_type="text", quantization=None):
        # 获取模型
        # 1. 从模型库中获取模型信息
        model_info = self.model_repository.get_model(model_name, model_type)
        
        # 2. 加载模型
        model = self.model_loader.load_model(
            model_info["path"],
            quantization=quantization
        )
        
        # 3. 分析模型
        model_analysis = self.model_analyzer.analyze(model)
        
        return {
            "model": model,
            "info": model_info,
            "analysis": model_analysis
        }
    
    def recommend_fine_tuning_strategy(self, model, dataset_info):
        # 推荐微调策略
        # 1. 分析模型和数据集
        model_type = model["analysis"]["type"]
        model_size = model["analysis"]["size"]
        dataset_size = dataset_info["size"]
        dataset_type = dataset_info["type"]
        
        # 2. 根据分析结果推荐策略
        if model_size > 10:  # 模型大小超过10B参数
            if dataset_size < 1000:
                return "qlora"  # 小数据集使用QLoRA
            else:
                return "lora"  # 大数据集使用LoRA
        elif model_type == "multimodal":
            return "multimodal_adapter"  # 多模态模型使用适配器微调
        else:
            return "full_finetuning"  # 小模型使用全参数微调

# 数据处理层核心代码示例
class DataProcessor:
    def __init__(self):
        self.data_loader = DataLoader()
        self.data_augmenter = DataAugmenter()
        self.data_annotation_helper = DataAnnotationHelper()
        self.data_validator = DataValidator()
    
    def process_dataset(self, dataset_path, dataset_type="text", augment=True, validate=True):
        # 处理数据集
        # 1. 加载数据集
        dataset = self.data_loader.load(dataset_path, dataset_type)
        
        # 2. 数据增强
        if augment:
            dataset = self.data_augmenter.augment(dataset, dataset_type)
        
        # 3. 数据标注辅助
        dataset = self.data_annotation_helper.improve_annotations(dataset, dataset_type)
        
        # 4. 数据验证
        if validate:
            validation_result = self.data_validator.validate(dataset, dataset_type)
            if not validation_result["valid"]:
                raise ValueError(f"Dataset validation failed: {validation_result['issues']}")
        
        return dataset
    
    def split_dataset(self, dataset, train_ratio=0.8, val_ratio=0.1, test_ratio=0.1):
        # 划分数据集
        return self.data_loader.split(dataset, train_ratio, val_ratio, test_ratio)

# 微调策略层核心代码示例
class FineTuningStrategy:
    def __init__(self):
        self.strategies = {
            "full_finetuning": FullFinetuning(),
            "lora": LoRAFinetuning(),
            "qlora": QLoRAFinetuning(),
            "adapter": AdapterFinetuning(),
            "multimodal_adapter": MultimodalAdapterFinetuning()
        }
    
    def get_strategy(self, strategy_name):
        # 获取微调策略
        if strategy_name not in self.strategies:
            raise ValueError(f"Unsupported fine-tuning strategy: {strategy_name}")
        return self.strategies[strategy_name]
    
    def execute_finetuning(self, model, dataset, strategy_name, hyperparameters=None):
        # 执行微调
        # 1. 获取微调策略
        strategy = self.get_strategy(strategy_name)
        
        # 2. 设置默认超参数（如果未提供）
        if hyperparameters is None:
            hyperparameters = strategy.get_default_hyperparameters()
        
        # 3. 执行微调
        return strategy.finetune(model["model"], dataset, hyperparameters)
    
    def register_strategy(self, strategy_name, strategy):
        # 注册新的微调策略
        self.strategies[strategy_name] = strategy

# 自动调参模块核心代码示例
class AutoHyperparameterTuner:
    def __init__(self):
        self.optimizer = BayesianOptimizer()
        self.evaluator = PerformanceEvaluator()
    
    def tune(self, model, dataset, strategy_name, hyperparameter_space=None, max_trials=20):
        # 自动调参
        # 1. 获取微调策略
        strategy = FineTuningStrategy().get_strategy(strategy_name)
        
        # 2. 设置超参数搜索空间
        if hyperparameter_space is None:
            hyperparameter_space = strategy.get_hyperparameter_space()
        
        # 3. 定义目标函数
        def objective(hyperparameters):
            # 执行微调
            result = strategy.finetune(model["model"], dataset["train"], hyperparameters)
            # 评估微调效果
            evaluation = self.evaluator.evaluate(result["model"], dataset["val"])
            return evaluation["score"]  # 返回评估分数作为优化目标
        
        # 4. 执行超参数优化
        best_hyperparameters, best_score = self.optimizer.optimize(
            objective,
            hyperparameter_space,
            max_trials=max_trials
        )
        
        # 5. 使用最佳超参数重新微调
        best_result = strategy.finetune(
            model["model"],
            dataset["train"],
            best_hyperparameters
        )
        
        # 6. 在测试集上评估最终模型
        test_evaluation = self.evaluator.evaluate(best_result["model"], dataset["test"])
        
        return {
            "best_hyperparameters": best_hyperparameters,
            "best_score": best_score,
            "best_model": best_result["model"],
            "test_evaluation": test_evaluation,
            "trials": self.optimizer.get_trials()
        }

# 克隆仓库
git clone https://github.com/FineTuneX/FineTuneX.git
cd FineTuneX

# 创建虚拟环境
python -m venv venv
source venv/bin/activate  # Linux/macOS
venv\Scripts\activate  # Windows

# 安装依赖
pip install -r requirements.txt

# 配置环境变量
export FINETUNEX_CONFIG="./config.yaml"
export FINETUNEX_LOG_LEVEL="INFO"

# 启动FineTuneX服务
python -m finetunex serve

# 导入FineTuneX
from finetunex import FineTuneX

# 初始化FineTuneX
finetunex = FineTuneX()

# 1. 选择预训练模型
model = finetunex.select_model(
    model_name="llama-3.2-70b",
    model_type="text",
    quantization="4bit"  # 使用4-bit量化
)

print(f"模型名称: {model['info']['name']}")
print(f"模型大小: {model['analysis']['size']}B参数")
print(f"推荐微调策略: {model['recommended_strategy']}")

# 2. 处理数据集
dataset = finetunex.process_dataset(
    dataset_path="data/my_dataset",
    dataset_type="text",
    augment=True,
    validate=True
)

# 3. 微调模型
finetune_result = finetunex.finetune(
    model=model,
    dataset=dataset,
    strategy="qlora",
    hyperparameters={
        "learning_rate": 1e-5,
        "batch_size": 32,
        "epochs": 3,
        "lora_rank": 64
    }
)

print(f"\n微调完成！")
print(f"训练时间: {finetune_result['training_time']}秒")
print(f"验证集分数: {finetune_result['validation_score']}")
print(f"测试集分数: {finetune_result['test_score']}")

# 4. 自动调参（可选）
# auto_tune_result = finetunex.auto_tune(
#     model=model,
#     dataset=dataset,
#     strategy="qlora",
#     max_trials=10
# )
# 
# print(f"\n自动调参完成！")
# print(f"最佳超参数: {auto_tune_result['best_hyperparameters']}")
# print(f"最佳分数: {auto_tune_result['best_score']}")
# print(f"测试集分数: {auto_tune_result['test_evaluation']['score']}")

# 5. 保存微调后的模型
finetunex.save_model(
    finetune_result['model'],
    save_path="models/my_finetuned_model"
)

print("\n模型已保存到models/my_finetuned_model")