Llama 3 近期重磅发布，发布了 8B 和 70B 参数量的模型，opencompass团队对 Llama 3 进行了评测！
书生·浦语和机智流社区同学投稿了 OpenCompass 评测 Llama 3，欢迎 Star。
https://github.com/open-compass/OpenCompass/
https://github.com/SmartFlowAI/Llama3-Tutorial/
本小节将带大家手把手用 opencompass 评测 Llama3 。
conda create -n llama3 python=3.10
conda activate llama3
conda install git
apt install git-lfs首先通过 OpenXLab 下载 Llama-3-8B-Instruct 这个模型。
mkdir -p ~/model
cd ~/model
git clone https://code.openxlab.org.cn/MrCat/Llama-3-8B-Instruct.git Meta-Llama-3-8B-Instruct或者软链接 InternStudio 中的模型
ln -s /root/share/new_models/meta-llama/Meta-Llama-3-8B-Instruct \
~/modelcd ~
git clone https://github.com/open-compass/opencompass opencompass
cd opencompass
pip install -e .遇到错误请运行：
pip install -r requirements.txt
pip install protobuf
export MKL_SERVICE_FORCE_INTEL=1
export MKL_THREADING_LAYER=GNU
下载数据集到 data/ 处
wget https://github.com/open-compass/opencompass/releases/download/0.2.2.rc1/OpenCompassData-core-20240207.zip
unzip OpenCompassData-core-20240207.zip
OpenCompass 预定义了许多模型和数据集的配置，你可以通过 工具 列出所有可用的模型和数据集配置。
# 列出所有配置
# python tools/list_configs.py
# 列出所有跟 llama (模型)及 mmlu（数据集） 相关的配置
python tools/list_configs.py llama ceval
python run.py --datasets ceval_gen --hf-path /root/model/Meta-Llama-3-8B-Instruct --tokenizer-path /root/model/Meta-Llama-3-8B-Instruct --tokenizer-kwargs padding_side='left' truncation='left' trust_remote_code=True --model-kwargs trust_remote_code=True device_map='auto' --max-seq-len 2048 --max-out-len 16 --batch-size 4 --num-gpus 1 --debug
遇到 ModuleNotFoundError: No module named 'rouge' 错误请运行：
git clone https://github.com/pltrdy/rouge
cd rouge
python setup.py install
命令解析
python run.py \
--datasets ceval_gen \
--hf-path /root/model/Meta-Llama-3-8B-Instruct \ # HuggingFace 模型路径
--tokenizer-path /root/model/Meta-Llama-3-8B-Instruct \ # HuggingFace tokenizer 路径（如果与模型路径相同，可以省略）
--tokenizer-kwargs padding_side='left' truncation='left' trust_remote_code=True \ # 构建 tokenizer 的参数
--model-kwargs device_map='auto' trust_remote_code=True \ # 构建模型的参数
--max-seq-len 2048 \ # 模型可以接受的最大序列长度
--max-out-len 16 \ # 生成的最大 token 数
--batch-size 4 \ # 批量大小
--num-gpus 1 \ # 运行模型所需的 GPU 数量
--debug
评测完成后，将会看到：
dataset version metric mode opencompass.models.huggingface.HuggingFace_meta-llama_Meta-Llama-3-8B-Instruct
---------------------------------------------- --------- ------------- ------ --------------------------------------------------------------------------------
ceval-computer_network db9ce2 accuracy gen 63.16
ceval-operating_system 1c2571 accuracy gen 63.16
ceval-computer_architecture a74dad accuracy gen 52.38
ceval-college_programming 4ca32a accuracy gen 62.16
ceval-college_physics 963fa8 accuracy gen 42.11
ceval-college_chemistry e78857 accuracy gen 29.17
ceval-advanced_mathematics ce03e2 accuracy gen 42.11
ceval-probability_and_statistics 65e812 accuracy gen 27.78
ceval-discrete_mathematics e894ae accuracy gen 25
ceval-electrical_engineer ae42b9 accuracy gen 32.43
ceval-metrology_engineer ee34ea accuracy gen 62.5
ceval-high_school_mathematics 1dc5bf accuracy gen 5.56
ceval-high_school_physics adf25f accuracy gen 26.32
ceval-high_school_chemistry 2ed27f accuracy gen 63.16
ceval-high_school_biology 8e2b9a accuracy gen 36.84
ceval-middle_school_mathematics bee8d5 accuracy gen 31.58
ceval-middle_school_biology 86817c accuracy gen 71.43
ceval-middle_school_physics 8accf6 accuracy gen 57.89
ceval-middle_school_chemistry 167a15 accuracy gen 80
ceval-veterinary_medicine b4e08d accuracy gen 52.17
ceval-college_economics f3f4e6 accuracy gen 45.45
ceval-business_administration c1614e accuracy gen 30.3
ceval-marxism cf874c accuracy gen 47.37
ceval-mao_zedong_thought 51c7a4 accuracy gen 50
ceval-education_science 591fee accuracy gen 51.72
ceval-teacher_qualification 4e4ced accuracy gen 72.73
ceval-high_school_politics 5c0de2 accuracy gen 68.42
ceval-high_school_geography 865461 accuracy gen 42.11
ceval-middle_school_politics 5be3e7 accuracy gen 57.14
ceval-middle_school_geography 8a63be accuracy gen 50
ceval-modern_chinese_history fc01af accuracy gen 52.17
ceval-ideological_and_moral_cultivation a2aa4a accuracy gen 78.95
ceval-logic f5b022 accuracy gen 40.91
ceval-law a110a1 accuracy gen 33.33
ceval-chinese_language_and_literature 0f8b68 accuracy gen 34.78
ceval-art_studies 2a1300 accuracy gen 54.55
ceval-professional_tour_guide 4e673e accuracy gen 55.17
ceval-legal_professional ce8787 accuracy gen 30.43
ceval-high_school_chinese 315705 accuracy gen 31.58
ceval-high_school_history 7eb30a accuracy gen 65
ceval-middle_school_history 48ab4a accuracy gen 59.09
ceval-civil_servant 87d061 accuracy gen 34.04
ceval-sports_science 70f27b accuracy gen 63.16
ceval-plant_protection 8941f9 accuracy gen 68.18
ceval-basic_medicine c409d6 accuracy gen 57.89
ceval-clinical_medicine 49e82d accuracy gen 54.55
ceval-urban_and_rural_planner 95b885 accuracy gen 52.17
ceval-accountant 002837 accuracy gen 44.9
ceval-fire_engineer bc23f5 accuracy gen 38.71
ceval-environmental_impact_assessment_engineer c64e2d accuracy gen 45.16
ceval-tax_accountant 3a5e3c accuracy gen 34.69
ceval-physician 6e277d accuracy gen 57.14
ceval-stem - naive_average gen 46.34
ceval-social-science - naive_average gen 51.52
ceval-humanities - naive_average gen 48.72
ceval-other - naive_average gen 50.05
ceval-hard - naive_average gen 32.65
ceval - naive_average gen 48.63在 config 下添加模型配置文件 eval_llama3_8b_demo.py
from mmengine.config import read_base
with read_base():
from .datasets.mmlu.mmlu_gen_4d595a import mmlu_datasets
datasets = [*mmlu_datasets]
from opencompass.models import HuggingFaceCausalLM
models = [
dict(
type=HuggingFaceCausalLM,
abbr='Llama3_8b', # 运行完结果展示的名称
path='/root/model/Meta-Llama-3-8B-Instruct', # 模型路径
tokenizer_path='/root/model/Meta-Llama-3-8B-Instruct', # 分词器路径
model_kwargs=dict(
device_map='auto',
trust_remote_code=True
),
tokenizer_kwargs=dict(
padding_side='left',
truncation_side='left',
trust_remote_code=True,
use_fast=False
),
pad_token_id=151643,
max_out_len=100,
max_seq_len=2048,
batch_size=16,
run_cfg=dict(num_gpus=1),
)
]评测完成后，将会看到：
dataset version metric mode Llama3_8b
------------------------------------------------- --------- -------- ------ -----------
lukaemon_mmlu_college_biology caec7d accuracy gen 66.67
lukaemon_mmlu_college_chemistry 520aa6 accuracy gen 37
lukaemon_mmlu_college_computer_science 99c216 accuracy gen 53
lukaemon_mmlu_college_mathematics 678751 accuracy gen 36
lukaemon_mmlu_college_physics 4f382c accuracy gen 48.04
lukaemon_mmlu_electrical_engineering 770ce3 accuracy gen 43.45
lukaemon_mmlu_astronomy d3ee01 accuracy gen 68.42
lukaemon_mmlu_anatomy 72183b accuracy gen 54.07
lukaemon_mmlu_abstract_algebra 2db373 accuracy gen 31
lukaemon_mmlu_machine_learning 0283bb accuracy gen 43.75
lukaemon_mmlu_clinical_knowledge cb3218 accuracy gen 58.87
lukaemon_mmlu_global_facts ab07b6 accuracy gen 39
lukaemon_mmlu_management 80876d accuracy gen 78.64
lukaemon_mmlu_nutrition 4543bd accuracy gen 72.55
lukaemon_mmlu_marketing 7394e3 accuracy gen 90.17
lukaemon_mmlu_professional_accounting 444b7f accuracy gen 49.65
lukaemon_mmlu_high_school_geography 0780e6 accuracy gen 75.25
lukaemon_mmlu_international_law cf3179 accuracy gen 62.81
lukaemon_mmlu_moral_scenarios f6dbe2 accuracy gen 38.66
lukaemon_mmlu_computer_security ce7550 accuracy gen 35
lukaemon_mmlu_high_school_microeconomics 04d21a accuracy gen 62.18
lukaemon_mmlu_professional_law 5f7e6c accuracy gen 47.91
lukaemon_mmlu_medical_genetics 881ef5 accuracy gen 62
lukaemon_mmlu_professional_psychology 221a16 accuracy gen 69.44
lukaemon_mmlu_jurisprudence 001f24 accuracy gen 69.44
lukaemon_mmlu_world_religions 232c09 accuracy gen 74.85
lukaemon_mmlu_philosophy 08042b accuracy gen 71.06
lukaemon_mmlu_virology 12e270 accuracy gen 43.98
lukaemon_mmlu_high_school_chemistry ae8820 accuracy gen 42.86
lukaemon_mmlu_public_relations e7d39b accuracy gen 60
lukaemon_mmlu_high_school_macroeconomics a01685 accuracy gen 57.95
lukaemon_mmlu_human_sexuality 42407c accuracy gen 74.05
lukaemon_mmlu_elementary_mathematics 269926 accuracy gen 28.84
lukaemon_mmlu_high_school_physics 93278f accuracy gen 26.49
lukaemon_mmlu_high_school_computer_science 9965a5 accuracy gen 63
lukaemon_mmlu_high_school_european_history eefc90 accuracy gen 74.55
lukaemon_mmlu_business_ethics 1dec08 accuracy gen 51
lukaemon_mmlu_moral_disputes a2173e accuracy gen 70.81
lukaemon_mmlu_high_school_statistics 8f3f3a accuracy gen 52.78
lukaemon_mmlu_miscellaneous 935647 accuracy gen 54.15
lukaemon_mmlu_formal_logic cfcb0c accuracy gen 42.86
lukaemon_mmlu_high_school_government_and_politics 3c52f9 accuracy gen 86.01
lukaemon_mmlu_prehistory bbb197 accuracy gen 64.2
lukaemon_mmlu_security_studies 9b1743 accuracy gen 75.51
lukaemon_mmlu_high_school_biology 37b125 accuracy gen 74.84
lukaemon_mmlu_logical_fallacies 9cebb0 accuracy gen 68.1
lukaemon_mmlu_high_school_world_history 048e7e accuracy gen 83.12
lukaemon_mmlu_professional_medicine 857144 accuracy gen 72.43
lukaemon_mmlu_high_school_mathematics ed4dc0 accuracy gen 31.48
lukaemon_mmlu_college_medicine 38709e accuracy gen 56.65
lukaemon_mmlu_high_school_us_history 8932df accuracy gen 82.84
lukaemon_mmlu_sociology c266a2 accuracy gen 76.12
lukaemon_mmlu_econometrics d1134d accuracy gen 55.26
lukaemon_mmlu_high_school_psychology 7db114 accuracy gen 65.14
lukaemon_mmlu_human_aging 82a410 accuracy gen 62.33
lukaemon_mmlu_us_foreign_policy 528cfe accuracy gen 70
lukaemon_mmlu_conceptual_physics 63588e accuracy gen 26.38opencompass 官方已经支持 Llama3
