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32b5948f4e
* add do sample demo for subjective dataset * fix strings * format --------- Co-authored-by: Leymore <zfz-960727@163.com>
116 lines
3.4 KiB
Python
116 lines
3.4 KiB
Python
from mmengine.config import read_base
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with read_base():
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from .datasets.subjective.subjective_cmp.subjective_corev2 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 Corev2Summarizer
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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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# -------------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=HuggingFaceChatGLM3,
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abbr='chatglm3-6b-hf',
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path='THUDM/chatglm3-6b',
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tokenizer_path='THUDM/chatglm3-6b',
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model_kwargs=dict(
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device_map='auto',
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trust_remote_code=True,
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),
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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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),
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generation_kwargs=dict(
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do_sample=True,
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),
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meta_template=api_meta_template,
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max_out_len=2048,
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max_seq_len=4096,
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batch_size=1,
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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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datasets = [*subjective_datasets]
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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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infer = dict(
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partitioner=dict(type=SizePartitioner, max_task_size=500),
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runner=dict(
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type=SlurmSequentialRunner,
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partition='llm_dev2',
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quotatype='auto',
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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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# -------------Evalation Stage ----------------------------------------
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## ------------- JudgeLLM Configuration
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judge_model = 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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## ------------- Evaluation Configuration
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eval = dict(
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partitioner=dict(
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type=SubjectiveSizePartitioner, mode='m2n', max_task_size=500, base_models=[gpt4], compare_models=models
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),
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runner=dict(
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type=SlurmSequentialRunner,
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partition='llm_dev2',
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quotatype='auto',
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max_num_workers=256,
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task=dict(type=SubjectiveEvalTask, judge_cfg=judge_model),
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),
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)
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summarizer = dict(type=Corev2Summarizer, match_method='smart')
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work_dir = 'outputs/corev2/'
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