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MBTIGPT_en_ESFP

Machine Mindset: An MBTI Exploration of Large Language Models

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Introduction

Requirements

• python 3.8 and above
• pytorch 1.12 and above, 2.0 and above are recommended
• CUDA 11.4 and above are recommended (this is for GPU users, flash-attention users, etc.)

Quickstart

• Using the HuggingFace Transformers library (single-turn dialogue):
  bash

  1import torch 2from transformers import AutoModelForCausalLM, AutoTokenizer 3from transformers.generation.utils import GenerationConfig 4 5tokenizer = AutoTokenizer.from_pretrained("FarReelAILab/Machine_Mindset_en_ESFP", use_fast=False, trust_remote_code=True) 6model = AutoModelForCausalLM.from_pretrained("FarReelAILab/Machine_Mindset_en_ESFP", device_map="auto", torch_dtype=torch.float16, trust_remote_code=True) 7model.generation_config = GenerationConfig.from_pretrained("FarReelAILab/Machine_Mindset_en_ESFP") 8 9messages = [] 10messages.append({"role": "user", "content": "What is your MBTI personality type?"}) 11response = model.chat(tokenizer, messages) 12print(response) 13 14messages.append({'role': 'assistant', 'content': response}) 15messages.append({"role": "user", "content": "After spending a day with a group of people, how do you feel when you return home?"}) 16response = model.chat(tokenizer, messages) 17print(response)
• Using the HuggingFace Transformers library (multi-turn dialogue):
  bash

  1import torch 2from transformers import AutoModelForCausalLM, AutoTokenizer 3from transformers.generation.utils import GenerationConfig 4tokenizer = AutoTokenizer.from_pretrained("FarReelAILab/Machine_Mindset_en_ESFP", use_fast=False, trust_remote_code=True) 5model = AutoModelForCausalLM.from_pretrained("FarReelAILab/Machine_Mindset_en_ESFP", device_map="auto", torch_dtype=torch.float16, trust_remote_code=True) 6model.generation_config = GenerationConfig.from_pretrained("FarReelAILab/Machine_Mindset_en_ESFP") 7messages = [] 8print("####Enter 'exit' to exit.") 9print("####Enter 'clear' to clear the chat history.") 10while True: 11 user=str(input("User:")) 12 if user.strip()=="exit": 13 break 14 elif user.strip()=="clear": 15 messages=[] 16 continue 17 messages.append({"role": "user", "content": user}) 18 response = model.chat(tokenizer, messages) 19 print("Assistant:", response) 20 messages.append({"role": "assistant", "content": str(response)})
• Use LLaMA-Factory (multi-round conversation)
  bash

  1git clone https://github.com/hiyouga/LLaMA-Factory.git 2cd LLaMA-Factory 3python ./src/cli_demo.py \ 4 --model_name_or_path /path_to_your_local_model \ 5 --template llama2

Citation

@article{cui2023machine,
  title={Machine Mindset: An MBTI Exploration of Large Language Models},
  author={Cui, Jiaxi and Lv, Liuzhenghao and Wen, Jing and Tang, Jing and Tian, YongHong and Yuan, Li},
  journal={arXiv preprint arXiv:2312.12999},
  year={2023}
}

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