pytorch gpt2 lm-head causal-lm transformers conversational license:mit text-generation
.gitattributes README.md config.json merges.txt pytorch_model.bin special_tokens_map.json tokenizer.json tokenizer_config.json vocab.json
---

thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png

tags:

- conversational

license: mit

---

# DialoGPT Trained on the Speech of a Game Character


Chat with the model:

```python

from transformers import AutoTokenizer, AutoModelWithLMHead

  

tokenizer = AutoTokenizer.from_pretrained("dead69/GTP-small-yoda")

model = AutoModelWithLMHead.from_pretrained("dead69/GTP-small-yoda")

# Let's chat for 4 lines

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=0.7,

        temperature=0.8

    )

    

    # pretty print last ouput tokens from bot

    print("Master YODA: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))

```