diff --git a/tests/test_models.py b/tests/test_models.py
new file mode 100644
index 0000000..b01b8c3
--- /dev/null
+++ b/tests/test_models.py
@@ -0,0 +1,75 @@
+import sys
+
+sys.path.insert(0, sys.path[0] + "/../")
+
+import os
+import pytest
+import torch
+import torch.nn as nn
+import numpy as np
+
+from abl.models.nn import LeNet5, SymbolNet
+from abl.models.basic_model import BasicModel
+
+
+class TestBasicModel(object):
+    @pytest.mark.parametrize("num_classes", [4, 10])
+    @pytest.mark.parametrize("image_size", [(28, 28, 1), (45, 45, 1)])
+    @pytest.mark.parametrize("cls", [LeNet5, SymbolNet])
+    @pytest.mark.parametrize("criterion", [nn.CrossEntropyLoss])
+    @pytest.mark.parametrize("optimizer", [torch.optim.RMSprop])
+    @pytest.mark.parametrize("device", [torch.device("cpu"), torch.device("cuda:0")])
+    def test_models(self, num_classes, image_size, cls, criterion, optimizer, device):
+        cls = cls(num_classes=num_classes, image_size=image_size)
+        criterion = criterion()
+        optimizer = optimizer(cls.parameters(), lr=0.001)
+
+        self.num_classes = num_classes
+        self.image_size = image_size
+        self.model = BasicModel(cls, criterion, optimizer, device)
+
+        self.data_X = [
+            np.random.rand(image_size[2], image_size[0], image_size[1]).astype(
+                np.float32
+            )
+            for i in range(5)
+        ]
+        self.data_y = np.random.randint(0, num_classes, (5,))
+
+        self._test_fit()
+        self._test_predict()
+        self._test_predict_proba()
+        self._test_val()
+        self._test_save()
+        self._test_load()
+
+    def _test_fit(self):
+        self.model.fit(X=self.data_X, y=self.data_y)
+
+    def _test_predict(self):
+        predict_result = self.model.predict(X=self.data_X)
+        assert predict_result.dtype == int
+        assert predict_result.shape == (5,)
+        assert (0 <= predict_result).all() and (predict_result < self.num_classes).all()
+
+    def _test_predict_proba(self):
+        predict_result = self.model.predict_proba(X=self.data_X)
+        assert predict_result.dtype == np.float32
+        assert predict_result.shape == (5, self.num_classes)
+        assert (0 <= predict_result).all() and (predict_result <= 1).all()
+
+    def _test_val(self):
+        accuracy = self.model.val(X=self.data_X, y=self.data_y)
+        assert type(accuracy) == float
+        assert 0 <= accuracy <= 1
+
+    def _test_save(self):
+        self.model.save(1, "results/test_models")
+        assert os.path.exists("results/test_models/1_net.pth")
+        assert os.path.exists("results/test_models/1_opt.pth")
+        os.remove("results/test_models/1_net.pth")
+        os.remove("results/test_models/1_opt.pth")
+
+    def _test_load(self):
+        self.model.save(1, "results/test_models")
+        self.model.load(1, "results/test_models")