eval_internlm_turbomind.py

from mmengine.config import read_base

from opencompass.models.turbomind import TurboMindModel

with read_base():
    # choose a list of datasets
    from opencompass.configs.datasets.ceval.ceval_gen_5f30c7 import \
        ceval_datasets
    from opencompass.configs.datasets.gsm8k.gsm8k_gen_1d7fe4 import \
        gsm8k_datasets
    from opencompass.configs.datasets.humaneval.humaneval_gen_8e312c import \
        humaneval_datasets
    from opencompass.configs.datasets.mmlu.mmlu_gen_a484b3 import mmlu_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
    # 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')), [])

# # config for internlm-7b model
internlm_7b = dict(
    type=TurboMindModel,
    abbr='internlm-7b-turbomind',
    path='internlm/internlm-7b',
    engine_config=dict(session_len=2048,
                       max_batch_size=32,
                       rope_scaling_factor=1.0),
    gen_config=dict(top_k=1, top_p=0.8, temperature=1.0, max_new_tokens=100),
    max_out_len=100,
    max_seq_len=2048,
    batch_size=32,
    concurrency=32,
    run_cfg=dict(num_gpus=1, num_procs=1),
)

# config for internlm-20b model
internlm_20b = dict(
    type=TurboMindModel,
    abbr='internlm-20b-turbomind',
    path='internlm/internlm-20b',
    engine_config=dict(session_len=2048,
                       max_batch_size=8,
                       rope_scaling_factor=1.0),
    gen_config=dict(top_k=1, top_p=0.8, temperature=1.0, max_new_tokens=100),
    max_out_len=100,
    max_seq_len=2048,
    batch_size=8,
    concurrency=8,
    run_cfg=dict(num_gpus=1, num_procs=1),
)

models = [internlm_20b]