diff --git a/examples/serious_mnist.py b/examples/serious_mnist.py
new file mode 100644
index 0000000000..4fbee4f79e
--- /dev/null
+++ b/examples/serious_mnist.py
@@ -0,0 +1,52 @@
+#!/usr/bin/env python
+# see https://github.com/Matuzas77/MNIST-0.17/blob/master/MNIST_final_solution.ipynb
+import os
+import sys
+sys.path.append(os.getcwd())
+sys.path.append(os.path.join(os.getcwd(), 'test'))
+
+from tinygrad.tensor import Tensor
+from tinygrad.nn import BatchNorm2D
+from tinygrad.utils import get_parameters
+import tinygrad.optim as optim
+
+# TODO: abstract this generic trainer out of the test
+from test_mnist import train as train_on_mnist
+
+GPU = os.getenv("GPU") is not None
+
+class SeriousModel:
+  def __init__(self):
+    self.blocks = 3
+    self.block_convs = 3
+    self.chans = 128
+
+    self.convs = [Tensor.uniform(self.chans, self.chans if i > 0 else 1, 3, 3) for i in range(self.blocks * self.block_convs)]
+    # TODO: Make batchnorm work at train time
+    #self.bn = [BatchNorm2D(self.chans) for i in range(3)]
+    self.fc = Tensor.uniform(self.chans, 10)
+
+  def forward(self, x):
+    x = x.reshape(shape=(-1, 1, 28, 28)) # hacks
+    for i in range(self.blocks):
+      for j in range(self.block_convs):
+        # TODO: should padding be used?
+        x = x.conv2d(self.convs[i*3+j]).relu()
+      #x = self.bn[i](x)
+      if i > 0:
+        x = x.avg_pool2d(kernel_size=(2,2))
+    # TODO: Add concat support to concat with max_pool2d
+    x = x.avg_pool2d(kernel_size=x.shape[2:4])
+    x = x.reshape(shape=(-1, x.shape[1]))
+    x = x.dot(self.fc).logsoftmax()
+    return x
+
+if __name__ == "__main__":
+  model = SeriousModel()
+  params = get_parameters(model)
+  print(len(params))
+  if GPU:
+    [x.cuda_() for x in params]
+  optimizer = optim.Adam(params, lr=0.001)
+  train_on_mnist(model, optimizer, steps=1875, BS=32, gpu=GPU)
+
diff --git a/tinygrad/utils.py b/tinygrad/utils.py
index 73de62f033..a1cc500157 100644
--- a/tinygrad/utils.py
+++ b/tinygrad/utils.py
@@ -16,6 +16,8 @@ def fetch(url):
   return dat
 
 def get_parameters(model):
+  if isinstance(model, Tensor):
+    return [model]
   parameters = []
   if hasattr(model, '__dict__'):
     for k,v in model.__dict__.items():