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138 lines
4.1 KiB
Python
138 lines
4.1 KiB
Python
from os import getenv as gv
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from opencompass.models import HuggingFaceCausalLM
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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_compare import subjective_datasets
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from opencompass.models import HuggingFaceCausalLM, HuggingFace, HuggingFaceChatGLM3, OpenAI
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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_size import SubjectiveSizePartitioner
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from opencompass.runners import LocalRunner
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from opencompass.runners import SlurmSequentialRunner
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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 CompassBenchSummarizer
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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=[dict(role='SYSTEM', api_role='SYSTEM')],
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)
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# -------------Inference Stage ----------------------------------------
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from opencompass.models import HuggingFacewithChatTemplate
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models = [
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dict(
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type=HuggingFacewithChatTemplate,
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abbr='internlm2-chat-7b-hf',
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path='internlm/internlm2-chat-7b',
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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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stop_words=['</s>', '<|im_end|>'],
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generation_kwargs=dict(
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do_sample=True,
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),
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)
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]
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datasets = [*subjective_datasets]
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infer = dict(
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partitioner=dict(type=NaivePartitioner),
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runner=dict(
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type=SlurmSequentialRunner,
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partition='llmeval',
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quotatype='reserved',
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max_num_workers=256,
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task=dict(type=OpenICLInferTask),
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),
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)
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gpt4 = dict(
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abbr='gpt4-turbo',
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type=OpenAI,
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path='gpt-4-1106-preview',
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key='', # The key will be obtained from $OPENAI_API_KEY, but you can write down your key here as well
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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=4096,
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batch_size=4,
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retry=20,
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temperature=1,
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) # Re-inference gpt4's predictions or you can choose to use the pre-commited gpt4's predictions
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# -------------Evalation Stage ----------------------------------------
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## ------------- JudgeLLM Configuration
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judge_models = [dict(
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abbr='GPT4-Turbo',
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type=OpenAI,
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path='gpt-4-1106-preview',
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key='', # The key will be obtained from $OPENAI_API_KEY, but you can write down your key here as well
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meta_template=api_meta_template,
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query_per_second=1,
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max_out_len=1024,
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max_seq_len=4096,
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batch_size=2,
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retry=20,
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temperature=0,
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)]
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judge_models = [
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dict(
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type=HuggingFacewithChatTemplate,
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abbr='internlm102b',
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path='/mnt/petrelfs/caomaosong/backup_hwfile/100bjudge_6w_epoch1/hf',
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max_out_len=1024,
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batch_size=8,
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run_cfg=dict(num_gpus=4),
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stop_words=['</s>', '<|im_end|>'],
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),
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dict(
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type=HuggingFacewithChatTemplate,
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abbr='internlm102b2',
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path='/mnt/petrelfs/caomaosong/backup_hwfile/100bjudge_6w_epoch1/hf',
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max_out_len=1024,
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batch_size=8,
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run_cfg=dict(num_gpus=4),
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stop_words=['</s>', '<|im_end|>'],
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),
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dict(
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type=HuggingFacewithChatTemplate,
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abbr='internlm102b3',
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path='/mnt/petrelfs/caomaosong/backup_hwfile/100bjudge_6w_epoch1/hf',
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max_out_len=1024,
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batch_size=8,
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run_cfg=dict(num_gpus=4),
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stop_words=['</s>', '<|im_end|>'],
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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=SubjectiveSizePartitioner,
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strategy='split',
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max_task_size=10000000,
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mode='m2n',
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infer_order='double',
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base_models=[gpt4],
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compare_models=models,
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judge_models=judge_models,
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),
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runner=dict(type=LocalRunner, max_num_workers=32, task=dict(type=SubjectiveEvalTask)),
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#given_pred = [{'abbr':'gpt4-turbo', 'path':''}]
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)
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work_dir = 'outputs/compassbench/'
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summarizer = dict(type=CompassBenchSummarizer, summary_type='half_add')
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