diff --git a/README.md b/README.md
index 03cee59505..c0fe260869 100644
--- a/README.md
+++ b/README.md
@@ -34,38 +34,27 @@ import torch.nn as nn
 
 class ExampleModel(RootModule):
     def __init__(self):
-        self.l1 = nn.Linear(100, 20)
+        # define model
     
     # ---------------
     # TRAINING
     def training_step(self, data_batch):
-        # your dataloader decides what each batch looks like
         x, y = data_batch
         y_hat = self.l1(x)
         loss = some_loss(y_hat)
         
-        tqdm_dic = {'train_loss': loss}
-        
-        # must return scalar, dict for logging
-        return loss_val, tqdm_dic
+        return loss_val, {'train_loss': loss}
     
     def validation_step(self, data_batch):
-        # same as training...
         x, y = data_batch
         y_hat = self.l1(x)
         loss = some_loss(y_hat)
         
-        # val specific
-        acc = calculate_acc(y_hat, y)
-        
-        tqdm_dic = {'train_loss': loss, 'val_acc': acc, 'whatever_you_want': 'a'}
-        return loss_val, tqdm_dic
+        return loss_val, {'val_loss': loss}
  
      def validation_end(self, outputs):
         total_accs = []
         
-        # given to you by the framework with all validation outputs.
-        # chance to collate
         for output in outputs:
             total_accs.append(output['val_acc'].item())
         
@@ -83,17 +72,14 @@ class ExampleModel(RootModule):
     def load_model_specific(self, checkpoint):
         # lightning loads for you. Here's your chance to say what you want to load
         self.load_state_dict(checkpoint['state_dict'])
-        pass
-    
     
     # ---------------
     # TRAINING CONFIG
     def configure_optimizers(self):
         # give lightning the list of optimizers you want to use.
         # lightning will call automatically
-        optimizer = self.choose_optimizer(self.hparams.optimizer_name, self.parameters(), {'lr': self.hparams.learning_rate}, 'optimizer')
-        self.optimizers = [optimizer]
-        return self.optimizers
+        optimizer = self.choose_optimizer('adam', self.parameters(), {'lr': self.hparams.learning_rate}, 'optimizer')
+        return [optimizer]
     
     # LIGHTING WILL USE THE LOADERS YOU DEFINE HERE
     @property