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59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
from mmengine.config import read_base
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with read_base():
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from opencompass.configs.datasets.subjective.judgerbench.judgerbench import judgerbench_datasets
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from opencompass.models import (HuggingFace, HuggingFaceCausalLM,
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HuggingFaceChatGLM3, OpenAI,
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TurboMindModelwithChatTemplate)
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from opencompass.partitioners import NaivePartitioner, SizePartitioner
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from opencompass.runners import LocalRunner, SlurmSequentialRunner
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from opencompass.tasks import OpenICLEvalTask, OpenICLInferTask
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api_meta_template = dict(round=[
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dict(role='HUMAN', api_role='HUMAN'),
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dict(role='BOT', api_role='BOT', generate=True),
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])
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# -------------Inference Stage ----------------------------------------
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# For subjective evaluation, we often set do sample for models
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models = [
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dict(
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type=TurboMindModelwithChatTemplate,
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abbr='CompassJudger-1-7B-Instruct',
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path='opencompass/CompassJudger-1-7B-Instruct',
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engine_config=dict(session_len=16384, max_batch_size=16, tp=1),
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gen_config=dict(top_k=1,
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temperature=1e-6,
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top_p=0.9,
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max_new_tokens=2048),
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max_seq_len=16384,
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max_out_len=2048,
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batch_size=16,
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run_cfg=dict(num_gpus=1),
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)
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]
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datasets = judgerbench_datasets
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infer = dict(
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partitioner=dict(type=NaivePartitioner),
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runner=dict(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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# -------------Evalation Stage ----------------------------------------
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## ------------- Evaluation Configuration
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eval = dict(
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partitioner=dict(
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type=NaivePartitioner,
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n=10,
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),
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runner=dict(type=LocalRunner,
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max_num_workers=16,
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task=dict(type=OpenICLEvalTask)),
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)
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work_dir = 'outputs/judgerbench/'
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