diff --git a/tests/models/mtlsd_test.py b/tests/models/mtlsd_test.py
new file mode 100644
index 0000000..b58454b
--- /dev/null
+++ b/tests/models/mtlsd_test.py
@@ -0,0 +1,26 @@
+import unittest
+import torch
+from autoseg.networks import setup_unet
+from autoseg.utils import neighborhood
+from autoseg.models import MTLSDModel
+
+
+class TestMTLSDModel(unittest.TestCase):
+
+    def setUp(self):
+        # Set up any required data or configuration for your tests
+        unet = setup_unet()
+        self.mtlsd_model = MTLSDModel(unet, unet.out_channels)
+
+    def test_forward(self):
+        # Test the forward method of STELARRModel
+        input_tensor = torch.randn((1, 1, 100, 100, 100))
+        lsds, affs = self.mtlsd_model(input_tensor)
+
+        # Check if the output tensors have the correct shapes
+        self.assertEqual(lsds.shape, (1, 10, 8, 8, 8))
+        self.assertEqual(affs.shape, (1, len(neighborhood), 8, 8, 8))
+
+        # Check if the values are within a reasonable range (this can be adjusted based on your model)
+        self.assertTrue(torch.all(lsds >= 0) and torch.all(lsds <= 1))
+        self.assertTrue(torch.all(affs >= 0) and torch.all(affs <= 1))
diff --git a/tests/models/stelarr_test.py b/tests/models/stelarr_test.py
index cb9ffc4..aac0bfa 100644
--- a/tests/models/stelarr_test.py
+++ b/tests/models/stelarr_test.py
@@ -18,9 +18,9 @@ def test_forward(self):
         lsds, affs, fake = self.stelarr_model(input_tensor)
 
         # Check if the output tensors have the correct shapes
-        self.assertEqual(lsds.shape, (1, 10, 60, 60, 60))  # Replace with actual output shape
-        self.assertEqual(affs.shape, (1, len(neighborhood), 60, 60, 60))  # Replace with actual output shape
-        self.assertEqual(fake.shape, (1, 1, 60, 60, 60))  # Replace with actual output shape
+        self.assertEqual(lsds.shape, (1, 10, 60, 60, 60))
+        self.assertEqual(affs.shape, (1, len(neighborhood), 60, 60, 60))
+        self.assertEqual(fake.shape, (1, 1, 60, 60, 60))
 
         # Check if the values are within a reasonable range (this can be adjusted based on your model)
         self.assertTrue(torch.all(lsds >= 0) and torch.all(lsds <= 1))