eval_internlm_lmdeploy_apiserver.py

from mmengine.config import read_base
from opencompass.models.turbomind_api import TurboMindAPIModel

with read_base():
    # choose a list of datasets
    from opencompass.configs.datasets.mmlu.mmlu_gen_a484b3 import mmlu_datasets
    from opencompass.configs.datasets.ceval.ceval_gen_5f30c7 import ceval_datasets
    from opencompass.configs.datasets.SuperGLUE_WiC.SuperGLUE_WiC_gen_d06864 import WiC_datasets
    from opencompass.configs.datasets.triviaqa.triviaqa_gen_2121ce import triviaqa_datasets
    from opencompass.configs.datasets.gsm8k.gsm8k_gen_1d7fe4 import gsm8k_datasets
    from opencompass.configs.datasets.humaneval.humaneval_gen_8e312c import humaneval_datasets
    # and output the results in a choosen format
    from opencompass.configs.summarizers.medium import summarizer

datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])

internlm_chat_20b = dict(
    type=TurboMindAPIModel,
    abbr='internlm-chat-20b-turbomind',
    api_addr='http://0.0.0.0:23333',
    max_out_len=100,
    max_seq_len=2048,
    batch_size=8,
    run_cfg=dict(num_gpus=1, num_procs=1),
)

internlm_chat_7b = dict(
    type=TurboMindAPIModel,
    abbr='internlm-chat-7b-turbomind',
    api_addr='http://0.0.0.0:23333',
    max_out_len=100,
    max_seq_len=2048,
    batch_size=16,
    run_cfg=dict(num_gpus=1, num_procs=1),
)

models = [internlm_chat_20b]