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74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
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from mmengine.config import read_base
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with read_base():
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from opencompass.configs.datasets.chinese_simpleqa.chinese_simpleqa_gen import csimpleqa_datasets
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from opencompass.models.openai_api import OpenAI
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from opencompass.runners import LocalRunner
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from opencompass.tasks.subjective_eval import SubjectiveEvalTask
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from opencompass.partitioners.sub_naive import SubjectiveNaivePartitioner
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from opencompass.models import HuggingFacewithChatTemplate
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from opencompass.summarizers import DefaultSubjectiveSummarizer
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# -------------Inference Stage ----------------------------------------
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models = [
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dict(
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type=HuggingFacewithChatTemplate,
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abbr='Qwen2.5-1.5B-Instruct',
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path='Qwen/Qwen2.5-1.5B-Instruct',
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model_kwargs=dict(
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device_map='auto',
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trust_remote_code=True,
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),
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tokenizer_kwargs=dict(
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padding_side='left',
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truncation_side='left',
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trust_remote_code=True,
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),
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generation_kwargs=dict(
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do_sample=True,
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),
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max_out_len=200,
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max_seq_len=4096,
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batch_size=8,
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run_cfg=dict(num_gpus=1, num_procs=1),
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)
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]
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datasets = sum([v for k, v in locals().items() if ('datasets' in k)], [])
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summarizer = dict(type=DefaultSubjectiveSummarizer)
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# -------------Evalation Stage ----------------------------------------
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## ------------- JudgeLLM Configuration
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api_meta_template = dict(
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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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)
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judge_models = [
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dict(
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# GPT4o
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abbr='gpt-4o-0513-global',
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type=OpenAI,
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# gpt-4o
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path='gpt-4o-0513-global',
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key='xxx', # provide OPENAI_API_KEY
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meta_template=api_meta_template,
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query_per_second=16,
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max_out_len=1000,
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batch_size=8,
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retry=3)
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]
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## ------------- Evaluation Configuration
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eval = dict(
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partitioner=dict(type=SubjectiveNaivePartitioner, models=models, judge_models=judge_models),
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runner=dict(type=LocalRunner, max_num_workers=16, task=dict(type=SubjectiveEvalTask)),
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)
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work_dir = 'outputs/chinese_simpleqa/'
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