model_cfg.py

from opencompass.models import HuggingFaceCausalLM, HuggingFace, HuggingFaceChatGLM3
from opencompass.models.openai_api import 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
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 = [
    dict(
        type=HuggingFaceChatGLM3,
        abbr='chatglm3-6b-hf',
        path='THUDM/chatglm3-6b',
        tokenizer_path='THUDM/chatglm3-6b',
        model_kwargs=dict(
            device_map='auto',
            trust_remote_code=True,
        ),
        tokenizer_kwargs=dict(
            padding_side='left',
            truncation_side='left',
            trust_remote_code=True,
        ),
        generation_kwargs=dict(
            do_sample=True,
        ),
        meta_template=api_meta_template,
        max_out_len=2048,
        max_seq_len=4096,
        batch_size=1,
        run_cfg=dict(num_gpus=1, num_procs=1),
    )
]


judge_model = dict(
        abbr='GPT4-Turbo',
        type=OpenAIAllesAPIN, 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
        url='',
        meta_template=api_meta_template,
        query_per_second=1,
        max_out_len=1024,
        max_seq_len=4096,
        batch_size=1,
        retry=30,
        temperature = 0
)

infer = dict(
    partitioner=dict(type=SizePartitioner, strategy='split', max_task_size=10000),
    runner=dict(
        type=SlurmSequentialRunner,
        partition='llmeval',
        quotatype='auto',
        max_num_workers=256,
        task=dict(type=OpenICLInferTask),
    ),
)
runner=dict(type=LocalRunner, max_num_workers=12, task=dict(type=SubjectiveEvalTask, judge_cfg=judge_model))

gpt4 = dict(
    abbr='gpt4-turbo',
    type=OpenAIAllesAPIN,
    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=2048,
    max_seq_len=4096,
    batch_size=4,
    retry=20,
    temperature=1,
) 
given_pred = [{'abbr':'gpt4-turbo', 'path':'your path'}]