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from mmengine.config import read_base
from opencompass.models import ZhiPuV2AI
from opencompass.partitioners import NaivePartitioner
from opencompass.runners.local_api import LocalAPIRunner
from opencompass.tasks import OpenICLInferTask
with read_base():
# from .datasets.collections.chat_medium import datasets
from ..summarizers.medium import summarizer
from ..datasets.ceval.ceval_gen import ceval_datasets
datasets = [
*ceval_datasets,
]
# needs a special postprocessor for all
# except 'gsm8k' and 'strategyqa'
from opencompass.utils import general_eval_wrapper_postprocess
for _dataset in datasets:
if _dataset['abbr'] not in ['gsm8k', 'strategyqa']:
if hasattr(_dataset['eval_cfg'], 'pred_postprocessor'):
_dataset['eval_cfg']['pred_postprocessor']['postprocess'] = _dataset['eval_cfg']['pred_postprocessor']['type']
_dataset['eval_cfg']['pred_postprocessor']['type'] = general_eval_wrapper_postprocess
else:
_dataset['eval_cfg']['pred_postprocessor'] = {'type': general_eval_wrapper_postprocess}
api_meta_template = dict(
round=[
dict(role='HUMAN', api_role='HUMAN'),
dict(role='BOT', api_role='BOT', generate=True),
],
)
models = [
dict(
abbr='glm4_notools',
type=ZhiPuV2AI,
path='glm-4',
key='xxxxxx',
generation_kwargs={
'tools': [
{
'type': 'web_search',
'web_search': {
'enable': False # turn off the search
}
}
]
},
meta_template=api_meta_template,
query_per_second=1,
max_out_len=2048,
max_seq_len=2048,
batch_size=8)
]
infer = dict(
partitioner=dict(type=NaivePartitioner),
runner=dict(
type=LocalAPIRunner,
max_num_workers=2,
concurrent_users=2,
task=dict(type=OpenICLInferTask)),
)
work_dir = "outputs/api_zhipu_v2/" |