def load_weight_hook(self, module: nn.Module, incompatible_keys):
        missing_keys = incompatible_keys.missing_keys

        # если нет dora_scale создаём через норму весов
        if "dora_scale" in missing_keys:
            missing_keys.remove("dora_scale")
            org_weight = self.org_module[0].weight.detach().clone().float()
            self.dora_norm_dims = org_weight.dim() - 1
            if self.wd_on_out:
                self.dora_scale = nn.Parameter(
                    torch.norm(
                        org_weight.reshape(org_weight.shape[0], -1),
                        dim=1,
                        keepdim=True,
                    ).reshape(org_weight.shape[0], *[1] * self.dora_norm_dims)
                )
            else:
                self.dora_scale = nn.Parameter(
                    torch.norm(
                        org_weight.transpose(1, 0).reshape(org_weight.shape[1], -1),
                        dim=1,
                        keepdim=True,
                    )
                    .reshape(org_weight.shape[1], *[1] * self.dora_norm_dims)
                    .transpose(1, 0)
                )
            self.wd = True

        # обработка scalar
        for key in list(missing_keys):
            if "scalar" in key:
                missing_keys.remove(key)
        if isinstance(self.scalar, nn.Parameter):
            self.scalar.data.copy_(torch.ones_like(self.scalar))
        elif getattr(self, "scalar", None) is not None:
            self.scalar.copy_(torch.ones_like(self.scalar))
        else:
            self.register_buffer(
                "scalar", torch.ones_like(self.scalar), persistent=False
            )