OpenCompass/configs/eval_subjective_bradleyterry.py

from mmengine.config import read_base

with read_base():
    from opencompass.configs.datasets.subjective.alpaca_eval.alpacav2_judgeby_gpt4_bradleyterry import (
        alpacav2_datasets,
    )
    from opencompass.configs.datasets.subjective.compassarena.compassarena_compare_bradleyterry import (
        compassarena_datasets,
    )

    from opencompass.configs.datasets.subjective.wildbench.wildbench_pair_judge_bradleyterry import (
        wildbench_datasets,
    )

    from opencompass.configs.models.hf_internlm.lmdeploy_internlm2_5_7b_chat import (
        models as lmdeploy_internlm2_5_7b_chat,
    )

    from opencompass.configs.models.hf_internlm.lmdeploy_internlm2_5_20b_chat import (
        models as lmdeploy_internlm2_5_20b_chat,
    )

    from opencompass.configs.models.qwen2_5.lmdeploy_qwen2_5_7b_instruct import (
        models as lmdeploy_qwen2_5_7b_instruct,
    )

    from opencompass.configs.models.qwen2_5.lmdeploy_qwen2_5_14b_instruct import (
        models as lmdeploy_qwen2_5_14b_instruct,
    )

    from opencompass.configs.models.qwen.lmdeploy_qwen2_7b_instruct import (
        models as lmdeploy_qwen2_7b_instruct,
    )

from opencompass.models import (
    HuggingFace,
    HuggingFaceCausalLM,
    HuggingFaceChatGLM3,
    OpenAI,
    TurboMindModelwithChatTemplate,
)
from opencompass.partitioners import NaivePartitioner, SizePartitioner
from opencompass.partitioners.sub_naive import SubjectiveNaivePartitioner
from opencompass.partitioners.sub_num_worker import SubjectiveNumWorkerPartitioner
from opencompass.partitioners.sub_size import SubjectiveSizePartitioner
from opencompass.runners import LocalRunner, SlurmSequentialRunner
from opencompass.summarizers import (
    CompassArenaBradleyTerrySummarizer,
    SubjectiveSummarizer,
)
from opencompass.tasks import OpenICLInferTask
from opencompass.tasks.subjective_eval import SubjectiveEvalTask

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

# -------------Inference Stage ----------------------------------------
# For subjective evaluation, we often set do sample for models
models = [
    *lmdeploy_internlm2_5_7b_chat,
    *lmdeploy_internlm2_5_20b_chat,
    *lmdeploy_qwen2_5_14b_instruct,
    *lmdeploy_qwen2_5_7b_instruct,
    *lmdeploy_qwen2_7b_instruct,
]


datasets = [
    *alpacav2_datasets,
    *compassarena_datasets,
    *wildbench_datasets,
]


infer = dict(
    partitioner=dict(type=NaivePartitioner),
    runner=dict(type=LocalRunner, max_num_workers=16, task=dict(type=OpenICLInferTask)),
)
# -------------Evalation Stage ----------------------------------------

## ------------- JudgeLLM Configuration
judge_models = [
    dict(
        type=TurboMindModelwithChatTemplate,
        abbr='CompassJudger-1-32B-Instruct',
        path='opencompass/CompassJudger-1-32B-Instruct',
        engine_config=dict(session_len=16384, max_batch_size=16, tp=4),
        gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=2048),
        max_seq_len=16384,
        max_out_len=2048,
        batch_size=16,
        run_cfg=dict(num_gpus=4),
    )
]

## ------------- Evaluation Configuration
eval = dict(
    partitioner=dict(
        type=SubjectiveNaivePartitioner,
        models=models,
        judge_models=judge_models,
    ),
    runner=dict(
        type=LocalRunner, max_num_workers=16, task=dict(type=SubjectiveEvalTask)
    ),
)

## ------------- Summary Configuration
# This step fits a Bradley-Terry model (statistical model) with an option
# to include style features and control variables based on groups
# (group variables must be available in the input dataset for each observation).
summarizer = dict(
    type=CompassArenaBradleyTerrySummarizer,
    rating_system='bradleyterry',
    num_bootstrap=100,
    num_cpu=None,
    with_control_vars=True,
    normalize_style_features=False,
    odds_ratio=True,
)

work_dir = 'outputs/subjective/bradleyterry'