diff --git a/freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py b/freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py
index ea7981405..b29d20112 100644
--- a/freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py
+++ b/freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py
@@ -74,16 +74,17 @@ class PyTorchMLPClassifier(BasePyTorchClassifier):
         model.to(self.device)
         optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
         criterion = torch.nn.CrossEntropyLoss()
-        init_model = self.get_init_model(dk.pair)
-        trainer = PyTorchModelTrainer(
-            model=model,
-            optimizer=optimizer,
-            criterion=criterion,
-            model_meta_data={"class_names": class_names},
-            device=self.device,
-            init_model=init_model,
-            data_convertor=self.data_convertor,
-            **self.trainer_kwargs,
-        )
+        # check if continual_learning is activated, and retreive the model to continue training
+        trainer = self.get_init_model(dk.pair)
+        if trainer is None:
+            trainer = PyTorchModelTrainer(
+                model=model,
+                optimizer=optimizer,
+                criterion=criterion,
+                model_meta_data={"class_names": class_names},
+                device=self.device,
+                data_convertor=self.data_convertor,
+                **self.trainer_kwargs,
+            )
         trainer.fit(data_dictionary, self.splits)
         return trainer
diff --git a/freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py b/freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py
index 64f0f4b03..6e1270102 100644
--- a/freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py
+++ b/freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py
@@ -69,15 +69,16 @@ class PyTorchMLPRegressor(BasePyTorchRegressor):
         model.to(self.device)
         optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
         criterion = torch.nn.MSELoss()
-        init_model = self.get_init_model(dk.pair)
-        trainer = PyTorchModelTrainer(
-            model=model,
-            optimizer=optimizer,
-            criterion=criterion,
-            device=self.device,
-            init_model=init_model,
-            data_convertor=self.data_convertor,
-            **self.trainer_kwargs,
-        )
+        # check if continual_learning is activated, and retreive the model to continue training
+        trainer = self.get_init_model(dk.pair)
+        if trainer is None:
+            trainer = PyTorchModelTrainer(
+                model=model,
+                optimizer=optimizer,
+                criterion=criterion,
+                device=self.device,
+                data_convertor=self.data_convertor,
+                **self.trainer_kwargs,
+            )
         trainer.fit(data_dictionary, self.splits)
         return trainer
diff --git a/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py b/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
index e760f6e68..5e84ada72 100644
--- a/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
+++ b/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
@@ -75,17 +75,18 @@ class PyTorchTransformerRegressor(BasePyTorchRegressor):
         model.to(self.device)
         optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
         criterion = torch.nn.MSELoss()
-        init_model = self.get_init_model(dk.pair)
-        trainer = PyTorchTransformerTrainer(
-            model=model,
-            optimizer=optimizer,
-            criterion=criterion,
-            device=self.device,
-            init_model=init_model,
-            data_convertor=self.data_convertor,
-            window_size=self.window_size,
-            **self.trainer_kwargs,
-        )
+        # check if continual_learning is activated, and retreive the model to continue training
+        trainer = self.get_init_model(dk.pair)
+        if trainer is None:
+            trainer = PyTorchTransformerTrainer(
+                model=model,
+                optimizer=optimizer,
+                criterion=criterion,
+                device=self.device,
+                data_convertor=self.data_convertor,
+                window_size=self.window_size,
+                **self.trainer_kwargs,
+            )
         trainer.fit(data_dictionary, self.splits)
         return trainer
 
diff --git a/freqtrade/freqai/torch/PyTorchModelTrainer.py b/freqtrade/freqai/torch/PyTorchModelTrainer.py
index a3b0d9b9c..a25fa45bc 100644
--- a/freqtrade/freqai/torch/PyTorchModelTrainer.py
+++ b/freqtrade/freqai/torch/PyTorchModelTrainer.py
@@ -25,7 +25,7 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
             optimizer: Optimizer,
             criterion: nn.Module,
             device: str,
-            init_model: Dict,
+            # init_model: Dict,
             data_convertor: PyTorchDataConvertor,
             model_meta_data: Dict[str, Any] = {},
             window_size: int = 1,
@@ -56,8 +56,8 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
         self.max_n_eval_batches: Optional[int] = kwargs.get("max_n_eval_batches", None)
         self.data_convertor = data_convertor
         self.window_size: int = window_size
-        if init_model:
-            self.load_from_checkpoint(init_model)
+        # if init_model:
+        #     self.load_from_checkpoint(init_model)
 
     def fit(self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]):
         """