LLM-微调：Peft库--get_peft_model()--＞在llm基座模型的基础上注入Lora模块（加载流程）【注入的Lora模块的初始参数是随机初始化的】

 一、site-packages-->peft-->mapping.py-->get_peft_model()

def get_peft_model(model: PreTrainedModel, peft_config: PeftConfig, adapter_name: str = "default") -> PeftModel:
    """
    Returns a Peft model object from a model and a config.

    Args:
        model ([`transformers.PreTrainedModel`]): Model to be wrapped.
        peft_config ([`PeftConfig`]): Configuration object containing the parameters of the Peft model.
    """
    model_config = getattr(model, "config", {"model_type": "custom"})
    if hasattr(model_config, "to_dict"):
        model_config = model_config.to_dict()

    peft_config.base_model_name_or_path = model.__dict__.get("name_or_path", None)

    if peft_config.task_type not in MODEL_TYPE_TO_PEFT_MODEL_MAPPING.keys() and not peft_config.is_prompt_learning:
        return PeftModel(model, peft_config, adapte