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81 lines
2.8 KiB
Python
81 lines
2.8 KiB
Python
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from mmengine.config import read_base
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from opencompass.partitioners import SizePartitioner
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from opencompass.runners import LocalRunner, SlurmRunner
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from opencompass.tasks import OpenICLInferTask
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from opencompass.models import OpenAI
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from opencompass.lagent.actions.ipython_interpreter import IPythonInterpreter
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from opencompass.lagent.agents.react import CIReAct
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from opencompass.models.lagent import CodeAgent
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from lagent.agents.react import ReActProtocol
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with read_base():
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from .datasets.CIBench.CIBench_gen_eb42f9 import cibench_datasets as datasets
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FORCE_STOP_PROMPT_EN = """You should directly give results based on history information."""
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FEWSHOT_INSTRUCTION = """\
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You are an assistant who can utilize external tools.
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{tool_description}
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To use a tool, please response with the following format:
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```
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{thought} Think what you need to solve, do you need to use tools?
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{action} The tool name, should be one of [{action_names}].
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{action_input} The input to the tool that you want to use.
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```
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The tool will give you response after your response using the following format:
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```
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{response} the results after call the tool.
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```
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Therefore DO NOT generate tool response by yourself.
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Also please follow the guidelines:
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1. Always use code interpreter to solve the problem.
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2. The generated codes should always in a markdown code block format.
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3. The generated codes will be executed in an ipython manner and the results will be cached.
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4. Your responded code should always be simple and only solves the problem in current step.
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Begin!
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"""
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models = [
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dict(
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abbr='gpt-3.5-turbo',
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type=CodeAgent,
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agent_type=CIReAct,
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mutli_rounds=True,
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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(
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type=IPythonInterpreter,
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description=
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'''It can run Python code in a manner as jupyter notebook. The code must be a valid code that contains only python method.
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'''),
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],
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protocol=dict(
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type=ReActProtocol,
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call_protocol=FEWSHOT_INSTRUCTION,
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force_stop=FORCE_STOP_PROMPT_EN,
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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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response=dict(role='RESPONSE', begin='Tool Response:', end='\n'),
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finish=dict(role='FINISH', begin='Final Answer:', end='\n'),
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),
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batch_size=8,
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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=50, gen_task_coef=1),
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runner=dict(
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type=SlurmRunner, max_num_workers=8, retry=2,
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task=dict(type=OpenICLInferTask)),
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)
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