Hi @Vimal0703
One more option to improve the speed is to use onnx_runtime, but at this moment we don’t have any tool/script which will let you import export MBart to onnx.
I’ve written a script for exporting T5 to onnx, something similar can be used for MBart as well
  
    
    
    Speeding up T5 inference  🤗Transformers
  
  
    seq2seq decoding is inherently slow and using onnx is one obvious  solution to speed it up. The onnxt5  package already provides one way to use onnx for t5. 
But if we export the complete T5 model to onnx, then we can’t use the past_key_values for decoding since for the first decoding step past_key_values will be None  and onnx doesn’t accept None input. Without past_key_values onnx won’t give any speed-up over torch for beam search. 
One other solution is to export the encoder and lm_head to on…
  


Another option is, we have also ported the M2M100 model, which is SOTA many-to-many translation model. The m2m100_418M smaller than MBart50, can give more speed-up.  Here’s an example of how to use M2M100, https://huggingface.co/transformers/master/model_doc/m2m_100.html#training-and-generation 7