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50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
from mmengine.config import read_base
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with read_base():
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from .models.qwen.hf_qwen_7b_chat import models as hf_qwen_7b_chat
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from .models.chatglm.hf_chatglm2_6b import models as hf_chatglm2_6b
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from .models.hf_internlm.hf_internlm_chat_7b import models as hf_internlm_chat_7b
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from .datasets.subjective_cmp.subjective_cmp import subjective_datasets
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from .summarizers.subjective import summarizer
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datasets = [*subjective_datasets]
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from opencompass.models import HuggingFaceCausalLM, HuggingFace, OpenAI
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from opencompass.partitioners.sub_naive import SubjectiveNaivePartitioner
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from opencompass.runners import LocalRunner
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from opencompass.tasks.subjective_eval import SubjectiveEvalTask
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models = [*hf_qwen_7b_chat, *hf_chatglm2_6b, *hf_internlm_chat_7b]
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api_meta_template = dict(
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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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reserved_roles=[
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dict(role='SYSTEM', api_role='SYSTEM'),
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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=SubjectiveNaivePartitioner,
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mode='all', # 新参数
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),
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runner=dict(
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type=LocalRunner,
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max_num_workers=2, # 支持并行比较
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task=dict(
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type=SubjectiveEvalTask, # 新 task，用来读入一对 model 的输入
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judge_cfg=dict(
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abbr='GPT4',
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type=OpenAI,
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path='gpt-4-0613',
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key='ENV',
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meta_template=api_meta_template,
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query_per_second=1,
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max_out_len=2048,
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max_seq_len=2048,
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batch_size=2),
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)),
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)
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