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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 ..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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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 |
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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/" |