diff --git a/src/skelcast/models/module.py b/src/skelcast/models/module.py
index 622e22a..9c534b4 100644
--- a/src/skelcast/models/module.py
+++ b/src/skelcast/models/module.py
@@ -28,6 +28,12 @@ def validation_step(self, *args, **kwargs):
         Implements a validation step of a module
         """
         pass
+    
+    def from_pretrained(model_path=None):
+        """
+        Implements a method to load a pretrained model
+        """
+        pass
 
     def gradient_flow(self):
         """
diff --git a/src/skelcast/models/transformers/sttf.py b/src/skelcast/models/transformers/sttf.py
index 6bbf8e1..afc56e5 100644
--- a/src/skelcast/models/transformers/sttf.py
+++ b/src/skelcast/models/transformers/sttf.py
@@ -257,4 +257,12 @@ def predict(self, sample, n_steps=10, observe_from_to=[10]):
             to_ += 1
             sample_input = sample[:, from_:to_, ...]
 
-    return torch.stack(forecasted, dim=1)
\ No newline at end of file
+    return torch.stack(forecasted, dim=1)
+  
+  def from_pretrained(self, model_path=None):
+    if model_path is None:
+      raise ValueError('`model_path` must be provided.')
+    checkpoint = torch.load(model_path)
+    model_state_dict = checkpoint['model_state_dict']
+    self.load_state_dict(model_state_dict)
+    return self
\ No newline at end of file
diff --git a/tools/visualize_skel_movement.py b/tools/visualize_skel_movement.py
index 8c7709a..6242f26 100644
--- a/tools/visualize_skel_movement.py
+++ b/tools/visualize_skel_movement.py
@@ -27,9 +27,10 @@
                             max_number_of_bodies=1, transforms=tf)
     model = SpatioTemporalTransformer(n_joints=25, d_model=256, n_blocks=3, n_heads=8, d_head=16, mlp_dim=512, loss_fn=nn.SmoothL1Loss(), dropout=0.5)
     # TODO: Remove the hard coding of the checkpoint path
-    checkpoint = torch.load('/home/kaseris/Documents/mount/checkpoints_forecasting/presto-class/checkpoint_epoch_16_2024-01-05_092620.pt')
-    model_state_dict = checkpoint['model_state_dict']
-    model.load_state_dict(model_state_dict)
+    # checkpoint = torch.load('/home/kaseris/Documents/mount/checkpoints_forecasting/presto-class/checkpoint_epoch_16_2024-01-05_092620.pt')
+    # model_state_dict = checkpoint['model_state_dict']
+    # model.load_state_dict(model_state_dict)
+    model.from_pretrained('/home/kaseris/Documents/mount/checkpoints_forecasting/presto-class/checkpoint_epoch_16_2024-01-05_092620.pt')
     model = model.to('cpu')
     skeleton, label = dataset[args.sample]
     seq_len, n_bodies, n_joints, n_dims = skeleton.shape