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