diff --git a/deepmd/pt/train/training.py b/deepmd/pt/train/training.py
index 62bc5a4c97..97d28cd4a1 100644
--- a/deepmd/pt/train/training.py
+++ b/deepmd/pt/train/training.py
@@ -519,8 +519,11 @@ def get_loss(loss_params, start_lr, _ntypes, _model):
                         model_params["type_map"],
                         model_params["new_type_map"],
                     )
-                    if hasattr(self.model, "fitting_net"):
-                        self.model.fitting_net.change_energy_bias(
+                    # TODO: need an interface instead of fetching fitting_net!!!!!!!!!
+                    if hasattr(self.model, "atomic_model") and hasattr(
+                        self.model.atomic_model, "fitting_net"
+                    ):
+                        self.model.atomic_model.fitting_net.change_energy_bias(
                             config,
                             self.model,
                             old_type_map,
@@ -530,7 +533,7 @@ def get_loss(loss_params, start_lr, _ntypes, _model):
                         )
                     elif isinstance(self.model, DPZBLModel):
                         # need to updated
-                        self.model.change_energy_bias()
+                        self.model.atomic_model.change_energy_bias()
                     else:
                         raise NotImplementedError
         if init_frz_model is not None:
diff --git a/deepmd/pt/train/wrapper.py b/deepmd/pt/train/wrapper.py
index c1040fb9e3..061cd777db 100644
--- a/deepmd/pt/train/wrapper.py
+++ b/deepmd/pt/train/wrapper.py
@@ -75,12 +75,12 @@ def share_params(self, shared_links, resume=False):
             shared_level_base = shared_base["shared_level"]
             if "descriptor" in class_type_base:
                 if class_type_base == "descriptor":
-                    base_class = self.model[model_key_base].__getattr__("descriptor")
+                    base_class = self.model[model_key_base].get_descriptor()
                 elif "hybrid" in class_type_base:
                     hybrid_index = int(class_type_base.split("_")[-1])
                     base_class = (
                         self.model[model_key_base]
-                        .__getattr__("descriptor")
+                        .get_descriptor()
                         .descriptor_list[hybrid_index]
                     )
                 else:
@@ -96,14 +96,12 @@ def share_params(self, shared_links, resume=False):
                         "descriptor" in class_type_link
                     ), f"Class type mismatched: {class_type_base} vs {class_type_link}!"
                     if class_type_link == "descriptor":
-                        link_class = self.model[model_key_link].__getattr__(
-                            "descriptor"
-                        )
+                        link_class = self.model[model_key_link].get_descriptor()
                     elif "hybrid" in class_type_link:
                         hybrid_index = int(class_type_link.split("_")[-1])
                         link_class = (
                             self.model[model_key_link]
-                            .__getattr__("descriptor")
+                            .get_descriptor()
                             .descriptor_list[hybrid_index]
                         )
                     else:
diff --git a/source/tests/pt/test_finetune.py b/source/tests/pt/test_finetune.py
index d21a44acc7..dd72eb4718 100644
--- a/source/tests/pt/test_finetune.py
+++ b/source/tests/pt/test_finetune.py
@@ -44,27 +44,29 @@ def test_finetune_change_energy_bias(self):
         else:
             model = get_model(self.model_config)
         if isinstance(model, EnergyModel):
-            model.fitting_net.bias_atom_e = torch.rand_like(
-                model.fitting_net.bias_atom_e
+            model.get_fitting_net().bias_atom_e = torch.rand_like(
+                model.get_fitting_net().bias_atom_e
             )
             energy_bias_before = deepcopy(
-                model.fitting_net.bias_atom_e.detach().cpu().numpy().reshape(-1)
+                model.get_fitting_net().bias_atom_e.detach().cpu().numpy().reshape(-1)
             )
             bias_atom_e_input = deepcopy(
-                model.fitting_net.bias_atom_e.detach().cpu().numpy().reshape(-1)
+                model.get_fitting_net().bias_atom_e.detach().cpu().numpy().reshape(-1)
             )
         elif isinstance(model, DPZBLModel):
-            model.dp_model.fitting_net.bias_atom_e = torch.rand_like(
-                model.dp_model.fitting_net.bias_atom_e
+            model.dp_model.get_fitting_net().bias_atom_e = torch.rand_like(
+                model.dp_model.get_fitting_net().bias_atom_e
             )
             energy_bias_before = deepcopy(
-                model.dp_model.fitting_net.bias_atom_e.detach()
+                model.dp_model.get_fitting_net()
+                .bias_atom_e.detach()
                 .cpu()
                 .numpy()
                 .reshape(-1)
             )
             bias_atom_e_input = deepcopy(
-                model.dp_model.fitting_net.bias_atom_e.detach()
+                model.dp_model.get_fitting_net()
+                .bias_atom_e.detach()
                 .cpu()
                 .numpy()
                 .reshape(-1)
diff --git a/source/tests/pt/test_training.py b/source/tests/pt/test_training.py
index db69a1bcea..e4403d2251 100644
--- a/source/tests/pt/test_training.py
+++ b/source/tests/pt/test_training.py
@@ -52,11 +52,11 @@ def test_trainable(self):
             fix_params["model"]["descriptor"]["trainable"] = True
             trainer_fix = get_trainer(fix_params)
             model_dict_before_training = deepcopy(
-                trainer_fix.model.fitting_net.state_dict()
+                trainer_fix.model.get_fitting_net().state_dict()
             )
             trainer_fix.run()
             model_dict_after_training = deepcopy(
-                trainer_fix.model.fitting_net.state_dict()
+                trainer_fix.model.get_fitting_net().state_dict()
             )
         else:
             trainer_fix = get_trainer(fix_params)