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29 lines
1.1 KiB
Python
29 lines
1.1 KiB
Python
from mmengine.config import read_base
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from opencompass.models.turbomind_tis import TurboMindTisModel
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with read_base():
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# choose a list of datasets
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from opencompass.configs.datasets.mmlu.mmlu_gen_a484b3 import mmlu_datasets
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from opencompass.configs.datasets.ceval.ceval_gen_5f30c7 import ceval_datasets
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from opencompass.configs.datasets.SuperGLUE_WiC.SuperGLUE_WiC_gen_d06864 import WiC_datasets
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from opencompass.configs.datasets.triviaqa.triviaqa_gen_2121ce import triviaqa_datasets
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from opencompass.configs.datasets.gsm8k.gsm8k_gen_1d7fe4 import gsm8k_datasets
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from opencompass.configs.datasets.humaneval.humaneval_gen_8e312c import humaneval_datasets
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# and output the results in a choosen format
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from opencompass.configs.summarizers.medium import summarizer
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datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
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models = [
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dict(
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type=TurboMindTisModel,
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abbr='internlm-chat-20b-turbomind',
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path='internlm',
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tis_addr='0.0.0.0:33337',
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max_out_len=100,
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max_seq_len=2048,
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batch_size=8,
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run_cfg=dict(num_gpus=1, num_procs=1),
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)
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]
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