diff --git a/README.md b/README.md
index ae37e2a18a..ac3cbac832 100644
--- a/README.md
+++ b/README.md
@@ -183,22 +183,30 @@ trainer = pl.Trainer()
 trainer.fit(autoencoder, DataLoader(train), DataLoader(val))
 ```
 
-#### And without changing a single line of code, you could run on GPUs
+#### And without changing a single line of code, you could run on GPU/TPUss
 ```python
 # 8 GPUs
 trainer = Trainer(max_epochs=1, gpus=8)
 
 # 256 GPUs
 trainer = Trainer(max_epochs=1, gpus=8, num_nodes=32)
+
+# TPUs
+trainer = Trainer(tpu_cores=8)
 ```
 
-Or TPUs
+#### And even export for production via onnx or torchscript
 ```python
-# Distributes TPU core training
-trainer = Trainer(tpu_cores=8)
+# torchscript
+autoencoder = LitAutoEncoder()
+torch.jit.save(autoencoder.to_torchscript(), "model.pt") 
 
-# Single TPU core training
-trainer = Trainer(tpu_cores=[1])
+# onnx
+with tempfile.NamedTemporaryFile(suffix='.onnx', delete=False) as tmpfile:
+    autoencoder = LitAutoEncoder()
+    input_sample = torch.randn((1, 64))
+    autoencoder.to_onnx(tmpfile.name, input_sample, export_params=True)
+    os.path.isfile(tmpfile.name)
 ```
 
 #### For advanced users, you can still own complex training loops
@@ -218,7 +226,6 @@ class LitAutoEncoder(pl.LightningModule):
         self.manual_backward(loss_b, opt_b)
         opt_b.step()
         opt_b.zero_grad()
-
 ```
 ---