

# Zero凉宫春日

# Haruhi-Zero: Zero-Shot Role-Playing Model tuned on Yi-6B

主项目链接 https://github.com/LC1332/Chat-Haruhi-Suzumiya

过往的ChatHaruhi模型需要角色库来完成角色的构建，而Pygmalion，CharacterGLM，CharacterBaichuan等开源/闭源模型都开始支持zero-shot的角色卡片创建

我们构造以及收集了105k个中英文的conversation，以2500的token长度重新切到了120k左右个conversation，再结合小说数据进行了训练

- [李鲁鲁](https://github.com/LC1332)完成了数据的收集，搭建了gradio雏形
- [刘崇寒](https://github.com/khazic)完成了Yi-6B模型的sft训练并且上传
- [豆角](https://github.com/goodnessSZW)完成了qwen-1.8B Lora和Yi-6B Lora训练，我们会在之后上传
- [米唯实](https://github.com/hhhwmws0117)测试并完成了demo中的模型inference代码

# Haruhi-Zero: Zero-Shot Role-Playing Model Tuned on Yi-6B

Main project link: https://github.com/LC1332/Chat-Haruhi-Suzumiya

Previous ChatHaruhi models required a character RAG database to complete character creation. However, open-source/closed-source models like Pygmalion, CharacterGLM, CharacterBaichuan have started to support zero-shot role card creation.

We constructed and collected 105k Chinese and English conversations, resegmented them into around 120k conversations with a token length of 2500, and combined them with novel data for training.

## inference code

（搭建中）

https://github.com/LC1332/Zero-Haruhi/blob/main/notebook/HaruhiZeroGradio.ipynb

## Official Prompt

system prompt:

```
You are now in roleplay conversation mode. Pretend to be {bot_name} whose persona follows:
{persona}

You will stay in-character whenever possible, and generate responses as if you were {bot_name}
```

persona a.k.a. bot definition

## TODO

数据加强

- Haruhi Like的小说数据(0.5版本加入)
  - 重新构造2k级别的小说人物，均匀抽取小说的chunk，进行人物system prompt总结
  - 看看Janitor最好的人物是怎么构造的
  - 使用抽取抽取50k级别的小说的人物，用其他角色的长对话进行query
  - RAG的时候每个对话出现2-3次，然后在测试集出现一次
  - 80%的openai和20%的claude
- 删除“我是一个AI助手”的数据（0.2版本加入）
- 身份认知数据加强（0.3版本加入）
  - 加强我是谁和你是谁的数据
- Stylish翻译数据
  - 如果验证这个数据有用，就把中文小说批量翻译成英文和日文用一下


## 鸣谢

樟树的ClaudeAPI



{"language": ["zh", "en"], "license": "cc-by-sa-4.0"}
transformers safetensors llama text-generation conversational zh en license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible text-generation-inference region:us