---
title: Ricky3
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/Ricky3"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/Ricky3.md"
repository: AI-ModelScope/Ricky3
last_updated: 2024-09-24
license: mit
model_type:
  - gpt2
architectures:
  - GPT2LMHeadModel
library_name:
  - transformer
inference_backends:
  - "deploy_task text/emb"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 1921
stars: 0
tags:
  - conversational
---

# Ricky3

> Ricky3 - AI-ModelScope 在 ModelScope 开源的模型。DialoGPT Trained on the Speech of a Game Character

AI-ModelScope/Ricky3 是 ModelScope 魔搭社区上的机器学习模型，采用 mit 许可，可用 deploy_task text/emb、sglang 0.5.2、vllm 0.9.2 部署。

- **Repository**: AI-ModelScope/Ricky3
- **License**: mit
- **Inference backends**: deploy_task text/emb, sglang 0.5.2, vllm 0.9.2
- **Tags**: conversational
- **Downloads**: 1921
- **Stars**: 0
- **Last updated**: 2024-09-24

Source: https://www.modelscope.cn/models/AI-ModelScope/Ricky3

---

# DialoGPT Trained on the Speech of a Game Character

This is an instance of [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small) trained on a game character, Neku Sakuraba from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script).

Chat with the model:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead
  
tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-small-neku")

model = AutoModelWithLMHead.from_pretrained("r3dhummingbird/DialoGPT-small-neku")

# 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("NekuBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```