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96 lines
2.7 KiB
Python
96 lines
2.7 KiB
Python
from mmengine.config import read_base
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with read_base():
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from opencompass.configs.datasets.OpenHuEval.HuLifeQA import (
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hu_life_qa_datasets,
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task_group_new,
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)
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from opencompass.configs.models.qwen2_5.lmdeploy_qwen2_5_7b_instruct import (
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models as lmdeploy_qwen2_5_7b_instruct_model, )
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from opencompass.configs.models.hf_internlm.lmdeploy_internlm3_8b_instruct import (
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models as lmdeploy_internlm3_8b_instruct_model, )
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from opencompass.configs.models.openai.gpt_4o_mini_20240718 import (
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models as gpt_4o_mini_20240718_model, )
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from opencompass.configs.models.qwq.lmdeploy_qwq_32b_preview import (
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models as lmdeploy_qwq_32b_preview_model, )
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from opencompass.models import OpenAI
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from opencompass.partitioners import (
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NumWorkerPartitioner,
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SubjectiveNumWorkerPartitioner,
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)
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from opencompass.runners import LocalRunner, SlurmSequentialRunner
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from opencompass.summarizers import WildBenchSingleSummarizer
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from opencompass.tasks import OpenICLInferTask
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from opencompass.tasks.subjective_eval import SubjectiveEvalTask
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api_meta_template = dict(round=[
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dict(role='SYSTEM', api_role='SYSTEM'),
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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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models = [
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# *gpt_4o_mini_20240718_model,
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*lmdeploy_qwen2_5_7b_instruct_model,
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*lmdeploy_internlm3_8b_instruct_model,
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*lmdeploy_qwq_32b_preview_model,
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]
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judge_models = [
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dict(
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abbr='GPT-4o-2024-08-06',
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type=OpenAI,
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path='gpt-4o-2024-08-06',
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key='ENV',
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openai_proxy_url='ENV',
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verbose=True,
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meta_template=api_meta_template,
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query_per_second=2,
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max_out_len=8192,
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max_seq_len=8192,
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batch_size=8,
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temperature=0,
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)
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]
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for ds in hu_life_qa_datasets:
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ds.update(dict(mode='singlescore', eval_mode='single'))
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del ds
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datasets = [*hu_life_qa_datasets]
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del hu_life_qa_datasets
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infer = dict(
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partitioner=dict(
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type=NumWorkerPartitioner,
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num_worker=8,
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),
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runner=dict(
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type=SlurmSequentialRunner,
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max_num_workers=16,
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task=dict(type=OpenICLInferTask),
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),
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)
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eval = dict(
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partitioner=dict(
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type=SubjectiveNumWorkerPartitioner,
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num_worker=8,
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models=models,
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judge_models=judge_models,
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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=SubjectiveEvalTask)),
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)
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summarizer = dict(
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type=WildBenchSingleSummarizer,
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customized_task_group_new=task_group_new,
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)
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work_dir = (
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'./outputs/' + __file__.split('/')[-1].split('.')[0] + '/'
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) # do NOT modify this line, yapf: disable, pylint: disable
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