diff --git a/examples/mlx/beautiful_mnist_mlx.py b/examples/other_mnist/beautiful_mnist_mlx.py
similarity index 100%
rename from examples/mlx/beautiful_mnist_mlx.py
rename to examples/other_mnist/beautiful_mnist_mlx.py
diff --git a/examples/other_mnist/beautiful_mnist_torch.py b/examples/other_mnist/beautiful_mnist_torch.py
new file mode 100644
index 00000000..862e48d6
--- /dev/null
+++ b/examples/other_mnist/beautiful_mnist_torch.py
@@ -0,0 +1,55 @@
+from tinygrad import dtypes
+from tinygrad.helpers import trange
+from tinygrad.nn.datasets import mnist
+import torch
+from torch import nn, optim
+
+class Model(nn.Module):
+  def __init__(self):
+    super().__init__()
+    self.c1 = nn.Conv2d(1, 32, 5)
+    self.c2 = nn.Conv2d(32, 32, 5)
+    self.bn1 = nn.BatchNorm2d(32)
+    self.m1 = nn.MaxPool2d(2)
+    self.c3 = nn.Conv2d(32, 64, 3)
+    self.c4 = nn.Conv2d(64, 64, 3)
+    self.bn2 = nn.BatchNorm2d(64)
+    self.m2 = nn.MaxPool2d(2)
+    self.lin = nn.Linear(576, 10)
+  def forward(self, x):
+    x = nn.functional.relu(self.c1(x))
+    x = nn.functional.relu(self.c2(x), 0)
+    x = self.m1(self.bn1(x))
+    x = nn.functional.relu(self.c3(x), 0)
+    x = nn.functional.relu(self.c4(x), 0)
+    x = self.m2(self.bn2(x))
+    return self.lin(torch.flatten(x, 1))
+
+if __name__ == "__main__":
+  mps_device = torch.device("mps")
+  X_train, Y_train, X_test, Y_test = mnist()
+  X_train = torch.tensor(X_train.float().numpy(), device=mps_device)
+  Y_train = torch.tensor(Y_train.cast(dtypes.int64).numpy(), device=mps_device)
+  X_test = torch.tensor(X_test.float().numpy(), device=mps_device)
+  Y_test = torch.tensor(Y_test.cast(dtypes.int64).numpy(), device=mps_device)
+
+  model = Model().to(mps_device)
+  optimizer = optim.Adam(model.parameters(), 1e-3)
+
+  loss_fn = nn.CrossEntropyLoss()
+  #@torch.compile
+  def step(samples):
+    X,Y = X_train[samples], Y_train[samples]
+    out = model(X)
+    loss = loss_fn(out, Y)
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    return loss
+
+  test_acc = float('nan')
+  for i in (t:=trange(70)):
+    samples = torch.randint(0, X_train.shape[0], (512,))  # putting this in JIT didn't work well
+    loss = step(samples)
+    if i%10 == 9: test_acc = ((model(X_test).argmax(axis=-1) == Y_test).sum() * 100 / X_test.shape[0]).item()
+    t.set_description(f"loss: {loss.item():6.2f} test_accuracy: {test_acc:5.2f}%")