src/peft/tuners/randlora/model.py [104:138]:
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    def __init__(self, model, config, adapter_name, low_cpu_mem_usage: bool = False) -> None:
        super().__init__(model, config, adapter_name, low_cpu_mem_usage=low_cpu_mem_usage)

    def _find_dim(self, config) -> tuple[int, int]:
        """
        Finds the largest input and output dimensions across linear layers that have been wrapped with RandLora.

        This will be used for determining the size of the shared randlora_A and randlora_B matrices.
        """
        model_config = self.get_model_config(self.model)

        peft_config = self._prepare_adapter_config(config, model_config)
        peft_config = _maybe_include_all_linear_layers(peft_config, self.model)

        largest_shape = None
        for key, module in self.model.named_modules():
            if not self._check_target_module_exists(peft_config, key):
                continue

            if isinstance(module, nn.Linear):
                module_shape = module.out_features, module.in_features
            elif isinstance(module, Conv1D):
                module_shape = module.weight.ds_shape if hasattr(module.weight, "ds_shape") else module.weight.shape
                module_shape = module_shape[::-1]
            else:
                continue

            if largest_shape is None:
                largest_shape = module_shape
                continue

            if module_shape != largest_shape:
                largest_shape = tuple(max(a, b) for a, b in zip(largest_shape, module_shape))

        if largest_shape is None:
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src/peft/tuners/vera/model.py [104:138]:
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    def __init__(self, model, config, adapter_name, low_cpu_mem_usage: bool = False) -> None:
        super().__init__(model, config, adapter_name, low_cpu_mem_usage=low_cpu_mem_usage)

    def _find_dim(self, config) -> tuple[int, int]:
        """
        Finds the largest input and output dimensions across linear layers that have been wrapped with VeRA.

        This will be used for determining the size of the shared vera_A and vera_B matrices.
        """
        model_config = self.get_model_config(self.model)

        peft_config = self._prepare_adapter_config(config, model_config)
        peft_config = _maybe_include_all_linear_layers(peft_config, self.model)

        largest_shape = None
        for key, module in self.model.named_modules():
            if not self._check_target_module_exists(peft_config, key):
                continue

            if isinstance(module, nn.Linear):
                module_shape = module.out_features, module.in_features
            elif isinstance(module, Conv1D):
                module_shape = module.weight.ds_shape if hasattr(module.weight, "ds_shape") else module.weight.shape
                module_shape = module_shape[::-1]
            else:
                continue

            if largest_shape is None:
                largest_shape = module_shape
                continue

            if module_shape != largest_shape:
                largest_shape = tuple(max(a, b) for a, b in zip(largest_shape, module_shape))

        if largest_shape is None:
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