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1from transformers import AutoModelForCausalLM, AutoTokenizer
2import torch
3tokenizer = AutoTokenizer.from_pretrained("vijayv500/DialoGPT-small-Big-Bang-Theory-Series-Transcripts")
4model = AutoModelForCausalLM.from_pretrained("vijayv500/DialoGPT-small-Big-Bang-Theory-Series-Transcripts")
5# Let's chat for 5 lines
6for step in range(5):
7 # encode the new user input, add the eos_token and return a tensor in Pytorch
8 new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
9 # append the new user input tokens to the chat history
10 bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
11 # generated a response while limiting the total chat history to 1000 tokens,
12 chat_history_ids = model.generate(
13 bot_input_ids, max_length=200,
14 pad_token_id=tokenizer.eos_token_id,
15 no_repeat_ngram_size=3,
16 do_sample=True,
17 top_k=100,
18 top_p=0.7,
19 temperature = 0.8
20 )
21 # pretty print last ouput tokens from bot
22 print("TBBT Bot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))