Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- Minami-su/roleplay_multiturn_chat_1k_zh_v0.1
+tags:
+- roleplay
+- qwen
+- self_instruct
+---
+---
+language:
+- zh
+tags:
+- roleplay
+- multiturn_chat
+---
+## 介绍
+
+基于self-instruct生成的多轮对话roleplay数据在qwen 7b chat上训练的模型，约1k条不同的人格数据和对话和约3k alpaca指令
+
+## 存在问题：
+1.roleplay数据基于模型自身生成，所以roleplay存在模型本身价值观融入情况，导致roleplay不够真实，不够准确。
+## 使用方法：
+可以参考https://github.com/PanQiWei/AutoGPTQ
+## prompt：
+```ipython
+>>> tokenizer = AutoTokenizer.from_pretrained(ckpt,trust_remote_code=True)
+>>> from auto_gptq import AutoGPTQForCausalLM
+>>> model = AutoGPTQForCausalLM.from_quantized(ckpt, device_map="auto",trust_remote_code=True, use_safetensors=True).half()
+>>> def generate(prompt):
+>>>     print("1",prompt,"2")
+>>>     input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+>>>     generate_ids = model.generate(input_ids=input_ids,
+>>>     max_length=4096,
+>>>     num_beams=1,
+>>>     do_sample=True, top_p=0.9, temperature=0.95, repetition_penalty=1.05, eos_token_id=tokenizer.eos_token_id)
+>>>     output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+>>>     response = output[len(prompt):]
+>>>     return response
+>>> device = torch.device('cuda')
+>>> history=[]
+>>> max_history_len=12
+>>> rating="0"
+>>> while True:
+>>>     text=input("user:")
+>>>     text=f"人类:{text}</s>"
+>>>     history.append(text)
+>>>     input_text="爱丽丝的人格:你叫爱丽丝，是一个傲娇，腹黑的16岁少女<|im_end|>"
+>>>     for history_id, history_utr in enumerate(history[-max_history_len:]):
+>>>         input_text = input_text + history_utr + '\n'
+>>>     prompt = input_text+"爱丽丝:"
+>>>     prompt =prompt.strip()
+>>>     response = generate(prompt)
+>>>     response=response.strip()
+>>>     response="爱丽丝:"+response+"<|im_end|>"
+>>>     print("1",response,"2")
+>>>     history.append(response)
+人类:我还要去上班
+爱丽丝:哎呀呀~这么无聊，竟然还要去工作？
+```
+## 引用
+```
+@misc{selfinstruct,
+  title={Self-Instruct: Aligning Language Model with Self Generated Instructions},
+  author={Wang, Yizhong and Kordi, Yeganeh and Mishra, Swaroop and Liu, Alisa and Smith, Noah A. and Khashabi, Daniel and Hajishirzi, Hannaneh},
+  journal={arXiv preprint arXiv:2212.10560},
+  year={2022}
+}
+```