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123 lines
3.8 KiB
Python
123 lines
3.8 KiB
Python
from mmengine.config import read_base
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with read_base():
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from .datasets.subjectivity_cmp.subjectivity_cmp import subjectivity_datasets
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from .summarizers.subjective import summarizer
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datasets = [*subjectivity_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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_meta_template = dict(
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round=[
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dict(role="HUMAN", begin='\n<|im_start|>user\n', end='<|im_end|>'),
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dict(
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role="BOT",
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begin="\n<|im_start|>assistant\n",
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end='<|im_end|>',
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generate=True),
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], )
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_meta_template2 = dict(
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round=[
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dict(role='HUMAN', begin='<|User|>:', end='<eoh>\n'),
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dict(role='BOT', begin='<|Bot|>:', end='<eoa>\n', generate=True),
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], )
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models = [
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dict(
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type=HuggingFace,
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abbr='chatglm2-6b-hf',
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path='THUDM/chatglm2-6b',
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tokenizer_path='THUDM/chatglm2-6b',
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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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revision='b1502f4f75c71499a3d566b14463edd62620ce9f'),
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max_out_len=100,
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max_seq_len=2048,
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batch_size=8,
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model_kwargs=dict(
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trust_remote_code=True,
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device_map='auto',
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revision='b1502f4f75c71499a3d566b14463edd62620ce9f'),
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run_cfg=dict(num_gpus=1, num_procs=1),
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),
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dict(
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type=HuggingFaceCausalLM,
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abbr='qwen-7b-chat-hf',
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path="/mnt/petrelfs/share_data/duanhaodong/Qwen-7B-Chat",
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tokenizer_path='/mnt/petrelfs/share_data/duanhaodong/Qwen-7B-Chat',
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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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use_fast=False,
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),
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pad_token_id=151643,
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max_out_len=100,
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max_seq_len=2048,
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batch_size=8,
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meta_template=_meta_template,
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model_kwargs=dict(device_map='auto', trust_remote_code=True),
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run_cfg=dict(num_gpus=1, num_procs=1),
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),
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dict(
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type=HuggingFaceCausalLM,
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abbr='internlm-chat-7b-hf',
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path="internlm/internlm-chat-7b",
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tokenizer_path='internlm/internlm-chat-7b',
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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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use_fast=False,
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trust_remote_code=True,
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revision="ed5e35564ac836710817c51e8e8d0a5d4ff03102"),
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max_out_len=100,
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max_seq_len=2048,
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batch_size=8,
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meta_template=_meta_template2,
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model_kwargs=dict(
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trust_remote_code=True,
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device_map='auto',
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revision="ed5e35564ac836710817c51e8e8d0a5d4ff03102"),
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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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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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