can i self-define the decoder when i user EncoderDecoderModel?
# ❓ Questions & Help
In my task, i have to define a decoder by myself, because the vocabulary is Chinese words.
But it seems the class method do not support that, i have to init the decoder from the pretrained model.

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**A link to original question on the forum/Stack Overflow**:
Hey @lonelydancer, 

you can define which decoder you want to use. It can be either a pretrained model or a randomly initialized model. The model just has to be part of `AutoModelForCausalLM` to be loaded into the EncoderDecoderModel. If you give me some more details on your use-case I can probably help you :-) <|||||>@patrickvonplaten 
Hi,here is my code.It seems ok.

tokenizer = BertTokenizer.from_pretrained(model_name)
tokenizer.bos_token = tokenizer.cls_token
tokenizer2 = BertTokenizer(vocab_file=voc_file, tokenize_chinese_chars=False) # Initialize tokenizer use my own vocab
tokenizer2.bos_token = tokenizer2.cls_token
decoder_model =  AutoModelForCausalLM.from_config(config=decoder_config)
config = EncoderDecoderConfig.from_encoder_decoder_configs(encoder_config, decoder_config)

<|||||>Why would you need two tokenizers ? 
Should the model encode in English and decode to Chinese?<|||||>@patrickvonplaten  inmy task，encoder model tokenizer is char level， and the decoder model is a word sequence.<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
