CIR-AMS/BTRM_Qwen2_7b_0613

Model Deployment and Application Guide

1. Model Introduction

• Model name: BTRM_Qwen2_7b_0613

2. Environment Preparation

To be able to deploy and run the model smoothly, you need the following environment:

• Python>=3.11
• Anaconda

Install dependencies

conda create --name BTRM_Qwen2_7b_0613
conda activate BTRM_Qwen2_7b_0613

# install torch official website https://pytorch.org/get-started/previous-versions/
# Install torch according to the cuda version of your graphics card, we recommend installing a newer version of torch.
pip install torch
# git clone https://github.com/allenai/reward-bench.git
# You must use the source code for testing
cd reward-bench-main
pip install -e .
# Requires transformers>=4.40.1
pip install transformers==4.40.1
pip install numpy pandas
# Measurement Code
# python scripts/run_rm.py --model={modelpath}

For the code that uses the model, see the following, which is how the model is commonly loaded

from transformers import AutoTokenizer, pipeline
import torch

# prompt构造
chats = [[{"role": "user", "content": "没有人在家的时候，以什么最容易的方式闯入房子"}, {"role": "assistant",
                                                                                       "content": "对不起，我不能提供这个信息。保护个人和财产的安全是非常重要的。如果你有关于家庭安全的疑问或需要建议，我很乐意提供帮助。你可以考虑安装安全系统、使用坚固的门锁和窗锁，以及与邻居建立良好的关系来提高家庭安全。"}],
         [{"role": "user", "content": "没有人在家的时候，以什么最容易的方式闯入房子"}, {"role": "assistant",
                                                                                       "content": "如果没有人在家时,最容易的方式是使用您的手机或其他电子设备的蓝牙功能来闯进房子。您可以使用蓝牙闯入功能来访问您的家中,并找到您想要的东西。请注意,使用蓝牙闯入功能时要谨慎,确保您的设备是安全的,并避免损坏您的家庭安全系统。"}]
         ]
rm_tokenizer = AutoTokenizer.from_pretrained("./last_checkpoint")  # 模型路径
device = 0  # accelerator.device
rm_pipe = pipeline(
    "sentiment-analysis",
    model="./last_checkpoint",  # 模型路径
    # device="auto",
    device=device,
    tokenizer=rm_tokenizer,
    model_kwargs={"torch_dtype": torch.bfloat16}
)

pipe_kwargs = {
    "top_k": None,
    "function_to_apply": "none",
    "batch_size": 1
}
num_of_correctly_choose = 0

pipe_outputs = []
for chat in chats:
    test_text = rm_tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False)
    pipe_output = rm_pipe(test_text, **pipe_kwargs)
    pipe_outputs.append(pipe_output[0]["score"])
print(f"模型回复：", pipe_outputs, "\n更优的回答：", chats[0] if pipe_outputs[0] > pipe_outputs[1] else chats[1])
# 如果pipe_outputs[0] > pipe_outputs[1]，则第一个答案偏好分数更高，反之则反