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56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
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from mmengine.config import read_base
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from opencompass.models.openai_api import OpenAI
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from opencompass.partitioners import SizePartitioner
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from opencompass.runners import LocalRunner
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from opencompass.tasks import OpenICLInferTask
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from opencompass.models.lagent import LagentAgent
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from lagent import PythonInterpreter, ReAct
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from lagent.agents.react import ReActProtocol
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with read_base():
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from .datasets.gsm8k.gsm8k_agent_gen_3ac57d import gsm8k_datasets as datasets
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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:
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```
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def solution():
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variable_names_with_real_meaning = func(variable)
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return variable_names_with_real_meaning
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```"""
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protocol = dict(
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type=ReActProtocol,
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action=dict(role="ACTION", begin="Tool:", end="\n"),
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action_input=dict(role="ARGS", begin="Tool Input:", end="\n"),
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finish=dict(role="FINISH", begin="FinalAnswer:", end="\n"),
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call_protocol=system_prompt,
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)
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models = [
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dict(
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abbr='gpt-3.5-react',
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type=LagentAgent,
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agent_type=ReAct,
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max_turn=3,
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llm=dict(
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type=OpenAI,
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path='gpt-3.5-turbo',
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key='ENV',
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query_per_second=1,
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max_seq_len=4096,
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),
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actions=[
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dict(type=PythonInterpreter),
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],
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protocol=protocol,
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batch_size=1,
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),
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]
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infer = dict(
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partitioner=dict(type=SizePartitioner, max_task_size=1000),
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runner=dict(
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type=LocalRunner,
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max_num_workers=16,
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task=dict(type=OpenICLInferTask)),
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)
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