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from mmengine.config import read_base
from opencompass.partitioners import SizePartitioner
from opencompass.runners import LocalRunner
from opencompass.tasks import OpenICLInferTask
from opencompass.models import OpenAI, HuggingFaceCausalLM
from opencompass.models.lagent import CodeAgent
with read_base():
from .datasets.math.math_gen_943d32 import math_datasets
from .datasets.gsm8k.gsm8k_gen_57b0b1 import gsm8k_datasets
datasets = []
datasets += gsm8k_datasets
datasets += math_datasets
models = [
dict(
abbr='gpt-3.5-react',
type=CodeAgent,
llm=dict(
type=OpenAI,
path='gpt-3.5-turbo',
key='ENV',
query_per_second=1,
max_seq_len=4096,
),
batch_size=8),
dict(
abbr='WizardCoder-Python-13B-V1.0-react',
type=CodeAgent,
llm=dict(
type=HuggingFaceCausalLM,
path="WizardLM/WizardCoder-Python-13B-V1.0",
tokenizer_path='WizardLM/WizardCoder-Python-13B-V1.0',
tokenizer_kwargs=dict(
padding_side='left',
truncation_side='left',
trust_remote_code=True,
),
max_seq_len=2048,
model_kwargs=dict(trust_remote_code=True, device_map='auto'),
),
batch_size=8,
run_cfg=dict(num_gpus=2, num_procs=1)),
]
infer = dict(
partitioner=dict(type=SizePartitioner, max_task_size=40000),
runner=dict(
type=LocalRunner, max_num_workers=16,
task=dict(type=OpenICLInferTask)),
)
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