mirror of
https://github.com/open-compass/opencompass.git
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85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
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from mmengine.config import read_base
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with read_base():
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from .datasets.subjective.compassbench.compassbench_checklist import (
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checklist_datasets,
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)
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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.runners import LocalRunner
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from opencompass.tasks import OpenICLInferTask
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from opencompass.tasks.subjective_eval import SubjectiveEvalTask
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# from opencompass.summarizers import SubjectiveSummarizer
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from opencompass.models import HuggingFacewithChatTemplate
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from opencompass.models import TurboMindModelwithChatTemplate
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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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)
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models = [
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# dict(
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# type=HuggingFacewithChatTemplate,
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# abbr="internlm2-chat-1.8b",
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# path="internlm/internlm2-chat-1_8b-sft",
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# max_out_len=1024,
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# batch_size=8,
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# run_cfg=dict(num_gpus=1),
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# ),
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# dict(
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# type=HuggingFacewithChatTemplate,
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# abbr="gpt4o",
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# path="internlm/internlm2-chat-1_8b-sft",
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# max_out_len=1024,
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# batch_size=8,
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# run_cfg=dict(num_gpus=1),
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# ),
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dict(
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type=TurboMindModelwithChatTemplate,
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abbr='internlm2-chat-1.8b-turbomind',
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path='internlm/internlm2-chat-1_8b',
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engine_config=dict(session_len=7168, max_batch_size=16, tp=1),
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gen_config=dict(top_k=1000, temperature=1, top_p=0.9, max_new_tokens=2048),
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max_seq_len=7168,
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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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dict(
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type=TurboMindModelwithChatTemplate,
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abbr='judgellm',
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path='internlm/internlm2-chat-1_8b',
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engine_config=dict(session_len=7168, max_batch_size=16, tp=1),
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gen_config=dict(top_k=1000, temperature=1, top_p=0.9, max_new_tokens=2048),
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max_seq_len=7168,
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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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# -------------Inference Stage ----------------------------------------
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# For subjective evaluation, we often set do sample for models
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datasets = [*checklist_datasets]
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infer = dict(
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partitioner=dict(type=NaivePartitioner),
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runner=dict(type=LocalRunner, max_num_workers=16, 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 = [models[0]]
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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(
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type=LocalRunner, max_num_workers=16, task=dict(type=SubjectiveEvalTask)
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),
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)
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# TODO summarizer to be implemented
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# summarizer = dict(type=SubjectiveSummarizer, function='subjective')
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work_dir = 'outputs/debug_checklist/'
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