diff --git a/test/models/test_real_world.py b/test/models/test_real_world.py
index 7f40f92c45..e255676a9b 100644
--- a/test/models/test_real_world.py
+++ b/test/models/test_real_world.py
@@ -135,30 +135,18 @@ class TestRealWorld(unittest.TestCase):
       #Device.DEFAULT = old_default
 
   @unittest.skipIf(Device.DEFAULT == "LLVM", "LLVM segmentation fault")
-  @unittest.skipIf(Device.DEFAULT in ["LLVM", "CLANG"] and CI, "too long on CI LLVM and CLANG")
-  def test_train_cifar_half(self):
+  # @unittest.skipIf(Device.DEFAULT in ["LLVM", "CLANG"] and CI, "too long on CI LLVM and CLANG")
+  def test_train_cifar_hyp(self):
     dtypes.default_float = dtypes.float16
     with Tensor.train():
       model = SpeedyResNet(Tensor.ones((12,3,2,2)))
-      optimizer = optim.SGD(get_parameters(model), lr=0.01, momentum=0.8, nesterov=True, weight_decay=0.15)
-      # optimizer = optim.SGD(get_parameters(model), lr=0.01, momentum=hyp['opt']['momentum'], nesterov=True, weight_decay=hyp['opt']['bias_decay'])
-      # initial_div_factor = hyp['opt']['initial_div_factor']
-      # final_lr_ratio = hyp['opt']['final_lr_ratio']
-      # pct_start = hyp['opt']['percent_start']
-      # lr_scheduler = OneCycleLR(optimizer, max_lr=hyp['opt']['bias_lr'], pct_start=pct_start, div_factor=initial_div_factor,
-      #                       final_div_factor=1./(initial_div_factor*final_lr_ratio), total_steps=4)
-      BS = 32 if CI else 512
-
-      @TinyJit
-      def train(X):
-        out = model(X)
-        loss = out.mean()
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
-        # lr_scheduler.step()
-
-      helper_test("train_cifar_half", lambda: (Tensor.randn(BS, 3, 32, 32),), train, (1.0/48)*BS, 145 if CI else 157)
+      optimizer = optim.SGD(get_parameters(model), lr=0.01, momentum=hyp['opt']['momentum'], nesterov=True, weight_decay=hyp['opt']['bias_decay'])
+      initial_div_factor = hyp['opt']['initial_div_factor']
+      final_lr_ratio = hyp['opt']['final_lr_ratio']
+      pct_start = hyp['opt']['percent_start']
+      lr_scheduler = OneCycleLR(optimizer, max_lr=hyp['opt']['bias_lr'], pct_start=pct_start, div_factor=initial_div_factor,
+                                final_div_factor=1./(initial_div_factor*final_lr_ratio), total_steps=4)
+      assert not np.isnan(lr_scheduler.min_lr.numpy()), "lr too small or initial_div_facotr too big for half"
 
     dtypes.default_float = dtypes.float32