eval_subjective_alpacaeval.py

from mmengine.config import read_base
with read_base():
    from .models.qwen.hf_qwen_7b_chat import models as hf_qwen_7b_chat
    from .models.qwen.hf_qwen_14b_chat import models as hf_qwen_14b_chat
    from .models.chatglm.hf_chatglm3_6b import models as hf_chatglm3_6b
    from .models.baichuan.hf_baichuan2_7b_chat import models as hf_baichuan2_7b
    from .models.hf_internlm.hf_internlm_chat_7b import models as hf_internlm_chat_7b
    from .models.hf_internlm.hf_internlm_chat_20b import models as hf_internlm_chat_20b
    from .datasets.subjective.alpaca_eval.alpacav1_judgeby_gpt4 import subjective_datasets as alpacav1
    from .datasets.subjective.alpaca_eval.alpacav2_judgeby_gpt4 import subjective_datasets as alpacav2

datasets = [*alpacav2]

from opencompass.models import HuggingFaceCausalLM, HuggingFace, HuggingFaceChatGLM3
from opencompass.models.openai_api import OpenAI, OpenAIAllesAPIN
from opencompass.partitioners import NaivePartitioner, SizePartitioner
from opencompass.partitioners.sub_naive import SubjectiveNaivePartitioner
from opencompass.partitioners.sub_size import SubjectiveSizePartitioner
from opencompass.runners import LocalRunner
from opencompass.runners import SlurmSequentialRunner
from opencompass.tasks import OpenICLInferTask
from opencompass.tasks.subjective_eval import SubjectiveEvalTask
from opencompass.summarizers import AlpacaSummarizer

models = [*hf_qwen_7b_chat, *hf_chatglm3_6b]

api_meta_template = dict(
    round=[
        dict(role='HUMAN', api_role='HUMAN'),
        dict(role='BOT', api_role='BOT', generate=True)
    ],
    reserved_roles=[
        dict(role='SYSTEM', api_role='SYSTEM'),
    ],
)

infer = dict(
    partitioner=dict(type=NaivePartitioner),
    runner=dict(
        type=SlurmSequentialRunner,
        partition='llmeval',
        quotatype='auto',
        max_num_workers=256,
        task=dict(type=OpenICLInferTask)),
)

judge_model = dict(
        abbr='GPT4-Turbo',
        type=OpenAI, path='gpt-4-1106-preview',
        key='',  # The key will be obtained from $OPENAI_API_KEY, but you can write down your key here as well
        meta_template=api_meta_template,
        query_per_second=1,
        max_out_len=1024,
        max_seq_len=4096,
        batch_size=2,
        retry=20,
        temperature = 0
)

eval = dict(
    partitioner=dict(
        type=SubjectiveSizePartitioner,
        max_task_size=1000,
        mode='m2n',
        base_models = [*hf_chatglm3_6b],
        compare_models = [*hf_qwen_7b_chat]
    ),
    runner=dict(
        type=SlurmSequentialRunner,
        partition='llmeval',
        quotatype='auto',
        max_num_workers=256,
        task=dict(
            type=SubjectiveEvalTask,
        judge_cfg=judge_model
        )),
)
work_dir = 'outputs/alpaca/'

summarizer = dict(
    type=AlpacaSummarizer, judge_type='v2'
)