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56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
from mmengine.config import read_base
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from opencompass.models.turbomind import TurboMindModel
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with read_base():
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# choose a list of datasets
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from opencompass.configs.datasets.ceval.ceval_gen_5f30c7 import \
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ceval_datasets
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from opencompass.configs.datasets.gsm8k.gsm8k_gen_1d7fe4 import \
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gsm8k_datasets
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from opencompass.configs.datasets.humaneval.humaneval_gen_8e312c import \
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humaneval_datasets
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from opencompass.configs.datasets.mmlu.mmlu_gen_a484b3 import mmlu_datasets
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from opencompass.configs.datasets.SuperGLUE_WiC.SuperGLUE_WiC_gen_d06864 import \
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WiC_datasets
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from opencompass.configs.datasets.triviaqa.triviaqa_gen_2121ce import \
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triviaqa_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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# # config for internlm-7b model
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internlm_7b = dict(
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type=TurboMindModel,
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abbr='internlm-7b-turbomind',
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path='internlm/internlm-7b',
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engine_config=dict(session_len=2048,
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max_batch_size=32,
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rope_scaling_factor=1.0),
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gen_config=dict(top_k=1, top_p=0.8, temperature=1.0, max_new_tokens=100),
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max_out_len=100,
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max_seq_len=2048,
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batch_size=32,
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concurrency=32,
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run_cfg=dict(num_gpus=1, num_procs=1),
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)
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# config for internlm-20b model
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internlm_20b = dict(
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type=TurboMindModel,
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abbr='internlm-20b-turbomind',
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path='internlm/internlm-20b',
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engine_config=dict(session_len=2048,
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max_batch_size=8,
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rope_scaling_factor=1.0),
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gen_config=dict(top_k=1, top_p=0.8, temperature=1.0, max_new_tokens=100),
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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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concurrency=8,
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run_cfg=dict(num_gpus=1, num_procs=1),
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)
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models = [internlm_20b]
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