for step in range(4): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # print(new_user_input_ids)

# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids

# generated a response while limiting the total chat history to 1000 tokens, 
chat_history_ids = model.generate(
    bot_input_ids, max_length=200,
    pad_token_id=tokenizer.eos_token_id,  
    no_repeat_ngram_size=3,       
    do_sample=True, 
    top_k=100, 
    top_p=0.1,
    temperature=1.2
)

# pretty print last ouput tokens from bot
print("Mae: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))