Encoder decoder config docs

src/transformers/configuration_encoder_decoder.py

@@ -56,6 +56,13 @@ class EncoderDecoderConfig(PretrainedConfig):
             >>> # Accessing the model configuration
             >>> config_encoder = model.config.encoder
             >>> config_decoder  = model.config.decoder
+
+            >>> 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"
 

src/transformers/modeling_encoder_decoder.py

@@ -128,6 +128,13 @@ def from_encoder_decoder_pretrained(
 
             >>> from transformers import EncoderDecoderModel
             >>> model = EncoderDecoderModel.from_encoder_decoder_pretrained('bert-base-uncased', 'bert-base-uncased') # initialize Bert2Bert
+
+        Example loading pretrained configs separately::
+            >>> from transformers import BertConfig, EncoderDecoderModel
+
+            >>> pretrained_model_name_or_path = 'bert-base-multilingual-cased'
+            >>> model = EncoderDecoderModel.from_encoder_decoder_pretrained(pretrained_model_name_or_path, pretrained_model_name_or_path)
+
         """
 
         kwargs_encoder = {