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67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
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from mmengine.config import read_base
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from opencompass.models import ZhiPuV2AI
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from opencompass.partitioners import NaivePartitioner
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from opencompass.runners.local_api import LocalAPIRunner
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from opencompass.tasks import OpenICLInferTask
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with read_base():
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# from .datasets.collections.chat_medium import datasets
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from ..summarizers.medium import summarizer
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from ..datasets.ceval.ceval_gen import ceval_datasets
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datasets = [
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*ceval_datasets,
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]
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# needs a special postprocessor for all
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# except 'gsm8k' and 'strategyqa'
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from opencompass.utils import general_eval_wrapper_postprocess
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for _dataset in datasets:
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if _dataset['abbr'] not in ['gsm8k', 'strategyqa']:
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if hasattr(_dataset['eval_cfg'], 'pred_postprocessor'):
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_dataset['eval_cfg']['pred_postprocessor']['postprocess'] = _dataset['eval_cfg']['pred_postprocessor']['type']
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_dataset['eval_cfg']['pred_postprocessor']['type'] = general_eval_wrapper_postprocess
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else:
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_dataset['eval_cfg']['pred_postprocessor'] = {'type': general_eval_wrapper_postprocess}
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api_meta_template = dict(
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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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)
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models = [
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dict(
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abbr='glm4_notools',
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type=ZhiPuV2AI,
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path='glm-4',
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key='xxxxxx',
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generation_kwargs={
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'tools': [
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{
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'type': 'web_search',
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'web_search': {
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'enable': False # turn off the search
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}
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}
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]
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},
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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(
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type=LocalAPIRunner,
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max_num_workers=2,
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concurrent_users=2,
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task=dict(type=OpenICLInferTask)),
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)
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work_dir = "outputs/api_zhipu_v2/"
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