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45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
from mmengine.config import read_base
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from opencompass.models import OpenAI
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from opencompass.partitioners import NaivePartitioner
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from opencompass.runners import LocalRunner
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from opencompass.tasks import OpenICLInferTask
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with read_base():
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from opencompass.configs.datasets.collections.chat_medium import datasets
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from opencompass.configs.summarizers.medium import summarizer
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# GPT4 needs a special humaneval postprocessor
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from opencompass.datasets.humaneval import humaneval_gpt_postprocess
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for _dataset in datasets:
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if _dataset['path'] == 'openai_humaneval':
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_dataset['eval_cfg']['pred_postprocessor'][
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'type'] = humaneval_gpt_postprocess
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api_meta_template = dict(round=[
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dict(role='HUMAN', api_role='HUMAN'),
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dict(role='BOT', api_role='BOT', generate=True),
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], )
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models = [
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dict(
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abbr='GPT4',
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type=OpenAI,
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path='gpt-4-0613',
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key=
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'ENV', # The key will be obtained from $OPENAI_API_KEY, but you can write down your key here as well
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meta_template=api_meta_template,
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query_per_second=1,
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max_out_len=2048,
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max_seq_len=2048,
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batch_size=8),
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]
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infer = dict(
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partitioner=dict(type=NaivePartitioner),
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runner=dict(type=LocalRunner,
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max_num_workers=4,
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task=dict(type=OpenICLInferTask)),
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)
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