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104 lines
4.7 KiB
Python
104 lines
4.7 KiB
Python
from mmengine.config import read_base
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with read_base():
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from opencompass.configs.datasets.subjective.compass_arena_subjective_bench.singleturn.pairwise_judge import compassarena_subjectivebench_singleturn_datasets
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from opencompass.configs.datasets.subjective.compass_arena_subjective_bench.multiturn.pairwise_judge import compassarena_subjectivebench_multiturn_datasets
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from opencompass.configs.models.hf_internlm.lmdeploy_internlm2_5_7b_chat import models as lmdeploy_internlm2_5_7b_chat
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from opencompass.configs.models.hf_internlm.lmdeploy_internlm2_5_20b_chat import models as lmdeploy_internlm2_5_20b_chat
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from opencompass.configs.models.hf_llama.lmdeploy_llama3_1_8b_instruct import models as lmdeploy_llama3_1_8b_instruct
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from opencompass.configs.models.hf_llama.lmdeploy_llama3_1_70b_instruct import models as lmdeploy_llama3_1_70b_instruct
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from opencompass.configs.models.qwen2_5.lmdeploy_qwen2_5_0_5b_instruct import models as lmdeploy_qwen2_5_0_5b_instruct
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from opencompass.configs.models.qwen2_5.lmdeploy_qwen2_5_1_5b_instruct import models as lmdeploy_qwen2_5_1_5b_instruct
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from opencompass.configs.models.qwen2_5.lmdeploy_qwen2_5_3b_instruct import models as lmdeploy_qwen2_5_3b_instruct
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from opencompass.configs.models.qwen2_5.lmdeploy_qwen2_5_7b_instruct import models as lmdeploy_qwen2_5_7b_instruct
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from opencompass.configs.models.qwen2_5.lmdeploy_qwen2_5_14b_instruct import models as lmdeploy_qwen2_5_14b_instruct
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from opencompass.configs.models.qwen2_5.lmdeploy_qwen2_5_32b_instruct import models as lmdeploy_qwen2_5_32b_instruct
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from opencompass.configs.models.qwen2_5.lmdeploy_qwen2_5_72b_instruct import models as lmdeploy_qwen2_5_72b_instruct
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from opencompass.configs.models.qwen.lmdeploy_qwen2_7b_instruct import models as lmdeploy_qwen2_7b_instruct
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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.partitioners.sub_naive import SubjectiveNaivePartitioner
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from opencompass.partitioners.sub_num_worker import \
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SubjectiveNumWorkerPartitioner
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from opencompass.partitioners.sub_size import SubjectiveSizePartitioner
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from opencompass.runners import LocalRunner, SlurmSequentialRunner
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from opencompass.summarizers import DefaultSubjectiveSummarizer
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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='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, temperature=1e-6, top_p=0.9, 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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models = [
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*lmdeploy_qwen2_5_14b_instruct, *lmdeploy_qwen2_5_32b_instruct,
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*lmdeploy_qwen2_5_7b_instruct, *lmdeploy_qwen2_7b_instruct
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]
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datasets = [
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*compassarena_subjectivebench_singleturn_datasets,
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*compassarena_subjectivebench_multiturn_datasets
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] # add datasets you want
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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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## ------------- JudgeLLM Configuration
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judge_models = [
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dict(
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type=TurboMindModelwithChatTemplate,
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abbr='CompassJudger-1-32B-Instruct',
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path='opencompass/CompassJudger-1-32B-Instruct',
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engine_config=dict(session_len=16384, max_batch_size=16, tp=4),
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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=4),
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)
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]
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## ------------- Evaluation Configuration
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eval = dict(
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partitioner=dict(
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type=SubjectiveNaivePartitioner,
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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(type=DefaultSubjectiveSummarizer, )
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work_dir = 'outputs/subjective/'
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