diff --git a/src/transformers/configuration_encoder_decoder.py b/src/transformers/configuration_encoder_decoder.py
index ae71dbecc1..785a4c654e 100644
--- a/src/transformers/configuration_encoder_decoder.py
+++ b/src/transformers/configuration_encoder_decoder.py
@@ -56,6 +56,15 @@ class EncoderDecoderConfig(PretrainedConfig):
             >>> # Accessing the model configuration
             >>> config_encoder = model.config.encoder
             >>> config_decoder  = model.config.decoder
+            >>> # set decoder config to causal lm
+            >>> config_decoder.is_decoder = True
+
+            >>> # Saving the model, including its configuration
+            >>> model.save_pretrained('my-model')
+
+            >>> # loading model and config from pretrained folder
+            >>> encoder_decoder_config = EncoderDecoderConfig.from_pretrained('my-model')
+            >>> model = EncoderDecoderModel.from_pretrained('my-model', config=encoder_decoder_config)
     """
     model_type = "encoder_decoder"
 
diff --git a/src/transformers/modeling_encoder_decoder.py b/src/transformers/modeling_encoder_decoder.py
index ec98a250d9..772ae74e22 100644
--- a/src/transformers/modeling_encoder_decoder.py
+++ b/src/transformers/modeling_encoder_decoder.py
@@ -127,7 +127,13 @@ class EncoderDecoderModel(PreTrainedModel):
         Examples::
 
             >>> from transformers import EncoderDecoderModel
-            >>> model = EncoderDecoderModel.from_encoder_decoder_pretrained('bert-base-uncased', 'bert-base-uncased') # initialize Bert2Bert
+            >>> # initialize a bert2bert from two pretrained BERT models. Note that the cross-attention layers will be randomly initialized
+            >>> model = EncoderDecoderModel.from_encoder_decoder_pretrained('bert-base-uncased', 'bert-base-uncased')
+            >>> # saving model after fine-tuning
+            >>> model.save_pretrained("./bert2bert")
+            >>> # load fine-tuned model
+            >>> model = EncoderDecoderModel.from_pretrained("./bert2bert")
+
         """
 
         kwargs_encoder = {