Method add_adapter

src/peft/peft_model.py:2621–2661

Add an adapter to the model based on the passed configuration. This adapter is not trained. To load a trained adapter, check out [`PeftModel.load_adapter`]. The name for the new adapter should be unique. The new adapter is not automatically set as the active adapt

(
        self,
        adapter_name: str,
        peft_config: PeftConfig,
        low_cpu_mem_usage: bool = False,
        autocast_adapter_dtype: bool = True,
    )

        )

    def add_adapter(
        self,
        adapter_name: str,
        peft_config: PeftConfig,
        low_cpu_mem_usage: bool = False,
        autocast_adapter_dtype: bool = True,
    ) -> None:
        """
        Add an adapter to the model based on the passed configuration.

        This adapter is not trained. To load a trained adapter, check out [`PeftModel.load_adapter`].

        The name for the new adapter should be unique.

        The new adapter is not automatically set as the active adapter. Use [`PeftModel.set_adapter`] to set the active
        adapter.

        Args:
            adapter_name (`str`):
                The name of the adapter to be added.
            peft_config ([`PeftConfig`]):
                The configuration of the adapter to be added.
            low_cpu_mem_usage (`bool`, `optional`, defaults to `False`):
                Create empty adapter weights on meta device. Useful to speed up the process when loading saved
                adapters. Don't use this option when creating a new PEFT adapter for training.
            autocast_adapter_dtype (`bool`, *optional*, defaults to `True`):
                Whether to autocast the adapter dtype. Defaults to `True`. Right now, this will only cast adapter
                weights using float16 and bfloat16 to float32, as this is typically required for stable training, and
                only affect select PEFT tuners. If set to `False`, the dtypes will stay the same as those of the
                corresponding layer.

        """
        # ensure that additional adapters also add the classifier layer to modules_to_save
        if hasattr(peft_config, "modules_to_save"):
            classifier_module_names = ["classifier", "score"]
            if peft_config.modules_to_save is None:
                peft_config.modules_to_save = classifier_module_names[:]
            else:
                peft_config.modules_to_save.extend(classifier_module_names)

        return super().add_adapter(adapter_name, peft_config, low_cpu_mem_usage=low_cpu_mem_usage)

    def forward(
        self,