Let's chat for 5 lines

for step in range(5): # 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')

# 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=1000, pad_token_id=tokenizer.eos_token_id)

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

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_microsoft__DialoGPT-small)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 25.02   |
| ARC (25-shot)         | 25.77          |
| HellaSwag (10-shot)   | 25.79    |
| MMLU (5-shot)         | 25.81         |
| TruthfulQA (0-shot)   | 47.49   |
| Winogrande (5-shot)   | 50.28   |
| GSM8K (5-shot)        | 0.0        |
| DROP (3-shot)         | 0.0         |