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from mmengine.config import read_base
from opencompass.models.openai_api import OpenAI
from opencompass.partitioners import SizePartitioner
from opencompass.runners import LocalRunner
from opencompass.tasks import OpenICLInferTask
from opencompass.models.lagent import LagentAgent
from opencompass.lagent.actions.python_interpreter import PythonInterpreter
from lagent import ReAct
from lagent.agents.react import ReActProtocol
with read_base():
from .datasets.gsm8k.gsm8k_agent_gen_be1606 import gsm8k_datasets
from .datasets.math.math_agent_gen_af2293 import math_datasets
from .datasets.MathBench.mathbench_agent_gen_568903 import mathbench_agent_datasets
from .summarizers.math_agent import summarizer
datasets = []
datasets += gsm8k_datasets
datasets += math_datasets
datasets += mathbench_agent_datasets
system_prompt = """You are a helpful assistant which use tools to solve mathematical reasoning questions. The code must be a function, and the function name must be 'solution'. For mathematics, please use code tool to calculate. The example format is as follows:
```
def solution():
variable_names_with_real_meaning = func(variable)
return variable_names_with_real_meaning
```"""
protocol = dict(
type=ReActProtocol,
action=dict(role="ACTION", begin="Tool:", end="\n"),
action_input=dict(role="ARGS", begin="Tool Input:", end="\n"),
finish=dict(role="FINISH", begin="FinalAnswer:", end="\n"),
call_protocol=system_prompt,
)
models = [
dict(
abbr='gpt-3.5-react',
type=LagentAgent,
agent_type=ReAct,
max_turn=3,
llm=dict(
type=OpenAI,
path='gpt-3.5-turbo',
key='ENV',
query_per_second=1,
max_seq_len=4096,
),
actions=[
dict(type=PythonInterpreter),
],
protocol=protocol,
batch_size=1,
),
]
infer = dict(
partitioner=dict(type=SizePartitioner, max_task_size=1000),
runner=dict(
type=LocalRunner,
max_num_workers=16,
task=dict(type=OpenICLInferTask)),
) |