diff --git a/harmonic/model.pyx b/harmonic/model.pyx
index fe9b73f4..c68c130b 100644
--- a/harmonic/model.pyx
+++ b/harmonic/model.pyx
@@ -68,17 +68,20 @@ class Model(metaclass=abc.ABCMeta):
             (double): Predicted log_e posterior value.
 
         """
-
-    @abc.abstractmethod
+        
     def is_fitted(self):
         """Specify whether model has been fitted.
-        
+            
         Returns:
 
             (bool): Whether the model has been fitted.
 
         """
 
+        return self.fitted
+
+
+
     def serialize(self, filename):
         """Serialize Model object.
 
@@ -227,18 +230,6 @@ class HyperSphere(Model):
         self.fitted             = False
 
 
-    def is_fitted(self):
-        """Specify whether model has been fitted.
-            
-        Returns:
-
-            (bool): Whether the model has been fitted.
-
-        """
-
-        return self.fitted
-
-
     def set_R(self, double R):
         """Set the radius of the hyper-sphere and calculate its volume.
 
@@ -663,17 +654,6 @@ class KernelDensityEstimate(Model):
 
         return
 
-
-    def is_fitted(self):
-        """Specify whether model has been fitted.
-            
-        Returns:
-
-            (bool): Whether the model has been fitted.
-
-        """
-
-        return self.fitted
         
 
     def set_scales(self, np.ndarray[double, ndim=2, mode="c"] X):
@@ -1409,18 +1389,6 @@ class ModifiedGaussianMixtureModel(Model):
         self.fitted              = False
 
 
-    def is_fitted(self):
-        """Specify whether model has been fitted.
-
-        Returns:
-
-            (bool): Whether the model has been fitted.
-
-        """
-
-        return self.fitted
-
-
     def set_weights(self, np.ndarray[double, ndim=1, mode="c"] weights_in):
         """Set the weights of the Gaussians.
         
diff --git a/harmonic/model_nf.py b/harmonic/model_nf.py
index 192121f2..c95d94be 100644
--- a/harmonic/model_nf.py
+++ b/harmonic/model_nf.py
@@ -119,17 +119,6 @@ def __init__(
         self.temperature = temperature
         self.flow = None
 
-    def is_fitted(self) -> bool:
-        """Specify whether model has been fitted.
-
-        Returns:
-
-            (bool): Whether the model has been fitted.
-
-        """
-
-        return self.fitted
-
     def create_train_state(self, rng):
         params = self.flow.init(rng, jnp.ones((1, self.ndim)))["params"]
         tx = optax.adam(self.learning_rate, self.momentum)