OpenCompass评测InterLM-1.8B实践

配置环境

有 8 G 显存即可

conda create -n opencompass python=3.8
conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 -c pytorch -c nvidia -y
cd ~
conda activate opencompass
git clone -b 0.2.4 https://github.com/open-compass/opencompass
cd opencompass
pip install -e .
apt-get update
apt-get install cmake
pip install -r requirements.txt
pip install protobuf

准备数据

解压评测数据集到 data/ 处

cp /share/temp/datasets/OpenCompassData-core-20231110.zip /root/opencompass/
unzip OpenCompassData-core-20231110.zip

InternLM和ceval 相关的配置文件

进入 opencompass 文件夹，使用下面的命令列出所有跟 InternLM 及 C-Eval 相关的配置

python tools/list_configs.py internlm ceval

打开 opencompass文件夹下configs/models/hf_internlm/的hf_internlm2_chat_1_8b.py ,贴入以下代码

from opencompass.models import HuggingFaceCausalLM


models = [
    dict(
        type=HuggingFaceCausalLM,
        abbr='internlm2-1.8b-hf',
        path="/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-1_8b",
        tokenizer_path='/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-1_8b',
        model_kwargs=dict(
            trust_remote_code=True,
            device_map='auto',
        ),
        tokenizer_kwargs=dict(
            padding_side='left',
            truncation_side='left',
            use_fast=False,
            trust_remote_code=True,
        ),
        max_out_len=100,
        min_out_len=1,
        max_seq_len=2048,
        batch_size=8,
        run_cfg=dict(num_gpus=1, num_procs=1),
    )
]

运行代码，进行评测，中间可能会出现缺少某些库，直接 pip 安装即可

python run.py
--datasets ceval_gen \ # 数据集准备
--models hf_internlm2_chat_1_8b \  # 模型准备
--debug

结果