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from mmengine.config import read_base
from opencompass.models import ByteDance
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,
]
models = [
dict(
abbr='skylark-pro-public',
type=ByteDance,
path='skylark-pro-public',
accesskey="xxxxxxx",
secretkey="xxxxxxx",
url='xxxxxx',
generation_kwargs={
'temperature': 0.7,
'top_p': 0.9,
'top_k': 0,
},
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_bytedance/" |