diff --git a/mambular/base_models/lightning_wrapper.py b/mambular/base_models/lightning_wrapper.py
index dbcd9e7..b26c643 100644
--- a/mambular/base_models/lightning_wrapper.py
+++ b/mambular/base_models/lightning_wrapper.py
@@ -37,7 +37,7 @@ def __init__(
         lss=False,
         family=None,
         loss_fct: callable = None,
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
         self.num_classes = num_classes
@@ -300,7 +300,7 @@ def configure_optimizers(self):
             A dictionary containing the optimizer and lr_scheduler configurations.
         """
         optimizer = torch.optim.Adam(
-            self.parameters(),
+            self.model.parameters(),
             lr=self.lr,
             weight_decay=self.weight_decay,
         )
diff --git a/mambular/models/sklearn_base_classifier.py b/mambular/models/sklearn_base_classifier.py
index 7373701..21e6c25 100644
--- a/mambular/models/sklearn_base_classifier.py
+++ b/mambular/models/sklearn_base_classifier.py
@@ -9,6 +9,7 @@
 from ..data_utils.datamodule import MambularDataModule
 from ..preprocessing import Preprocessor
 import numpy as np
+from lightning.pytorch.callbacks import ModelSummary
 
 
 class SklearnBaseClassifier(BaseEstimator):
@@ -367,12 +368,16 @@ def fit(
         )
 
         # Initialize the trainer and train the model
-        trainer = pl.Trainer(
+        self.trainer = pl.Trainer(
             max_epochs=max_epochs,
-            callbacks=[early_stop_callback, checkpoint_callback],
+            callbacks=[
+                early_stop_callback,
+                checkpoint_callback,
+                ModelSummary(max_depth=2),
+            ],
             **trainer_kwargs
         )
-        trainer.fit(self.model, self.data_module)
+        self.trainer.fit(self.model, self.data_module)
 
         best_model_path = checkpoint_callback.best_model_path
         if best_model_path:
diff --git a/mambular/models/sklearn_base_lss.py b/mambular/models/sklearn_base_lss.py
index 3298cff..5855b2a 100644
--- a/mambular/models/sklearn_base_lss.py
+++ b/mambular/models/sklearn_base_lss.py
@@ -31,6 +31,7 @@
     PoissonDistribution,
     StudentTDistribution,
 )
+from lightning.pytorch.callbacks import ModelSummary
 
 
 class SklearnBaseLSS(BaseEstimator):
@@ -409,12 +410,16 @@ def fit(
         )
 
         # Initialize the trainer and train the model
-        trainer = pl.Trainer(
+        self.trainer = pl.Trainer(
             max_epochs=max_epochs,
-            callbacks=[early_stop_callback, checkpoint_callback],
+            callbacks=[
+                early_stop_callback,
+                checkpoint_callback,
+                ModelSummary(max_depth=2),
+            ],
             **trainer_kwargs
         )
-        trainer.fit(self.model, self.data_module)
+        self.trainer.fit(self.model, self.data_module)
 
         best_model_path = checkpoint_callback.best_model_path
         if best_model_path:
diff --git a/mambular/models/sklearn_base_regressor.py b/mambular/models/sklearn_base_regressor.py
index c909641..19a4560 100644
--- a/mambular/models/sklearn_base_regressor.py
+++ b/mambular/models/sklearn_base_regressor.py
@@ -8,6 +8,7 @@
 from ..base_models.lightning_wrapper import TaskModel
 from ..data_utils.datamodule import MambularDataModule
 from ..preprocessing import Preprocessor
+from lightning.pytorch.callbacks import ModelSummary
 
 
 class SklearnBaseRegressor(BaseEstimator):
@@ -356,12 +357,16 @@ def fit(
         )
 
         # Initialize the trainer and train the model
-        trainer = pl.Trainer(
+        self.trainer = pl.Trainer(
             max_epochs=max_epochs,
-            callbacks=[early_stop_callback, checkpoint_callback],
+            callbacks=[
+                early_stop_callback,
+                checkpoint_callback,
+                ModelSummary(max_depth=2),
+            ],
             **trainer_kwargs
         )
-        trainer.fit(self.model, self.data_module)
+        self.trainer.fit(self.model, self.data_module)
 
         best_model_path = checkpoint_callback.best_model_path
         if best_model_path: