Method load_adapter

src/peft/peft_model.py:1324–1495

Load a trained adapter into the model. 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: model_id (`str` or `os.PathLike

(
        self,
        model_id: Union[str, os.PathLike],
        adapter_name: str,
        is_trainable: bool = False,
        torch_device: Optional[str] = None,
        autocast_adapter_dtype: bool = True,
        ephemeral_gpu_offload: bool = False,
        low_cpu_mem_usage: bool = False,
        key_mapping: Optional[dict[str, str]] = None,
        **kwargs: Any,
    )

                warnings.warn(msg)

    def load_adapter(
        self,
        model_id: Union[str, os.PathLike],
        adapter_name: str,
        is_trainable: bool = False,
        torch_device: Optional[str] = None,
        autocast_adapter_dtype: bool = True,
        ephemeral_gpu_offload: bool = False,
        low_cpu_mem_usage: bool = False,
        key_mapping: Optional[dict[str, str]] = None,
        **kwargs: Any,
    ):
        """
        Load a trained adapter into the model.

        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:
            model_id (`str` or `os.PathLike`):
                The name of the PEFT configuration to use. Can be either:
                    - A string, the `model id` of a PEFT configuration hosted inside a model repo on the Hugging Face
                      Hub.
                    - A path to a directory containing a PEFT configuration file saved using the `save_pretrained`
                      method (`./my_peft_config_directory/`).
            adapter_name (`str`):
                The name of the adapter to be added.
            is_trainable (`bool`, *optional*, defaults to `False`):
                Whether the adapter should be trainable or not. If `False`, the adapter will be frozen and can only be
                used for inference.
            torch_device (`str`, *optional*, defaults to None):
                The device to load the adapter on. If `None`, the device will be inferred.
            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.
            ephemeral_gpu_offload (`bool`, *optional*, defaults to `False`):
                Whether to use ephemeral GPU offloading for partially loaded modules. Defaults to `False`.
            low_cpu_mem_usage (`bool`, `optional`, defaults to `False`):
                Create empty adapter weights on meta device before loading the saved weights. Useful to speed up the
                process.
            key_mapping (dict, *optional*, defaults to None)
                Extra mapping of PEFT `state_dict` keys applied before loading the `state_dict`. When this mapping is
                applied, the PEFT-specific `"base_model.model"` prefix is removed beforehand and the adapter name (e.g.
                `"default"`) is not inserted yet. Only pass this argument if you know what you're doing.
            kwargs: (`optional`):
                Additional arguments to modify the way the adapter is loaded, e.g. the token for Hugging Face Hub.
        """
        from .mapping import PEFT_TYPE_TO_CONFIG_MAPPING

        hf_hub_download_kwargs, kwargs = self._split_kwargs(kwargs)
        if torch_device is None:
            torch_device = infer_device()

        if adapter_name not in self.peft_config: