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02a0a4e857
* add mt-bench-101 * add readme and requirements * add mt-bench-101 data * Update readme_mtbench101.md * update readme * update leaderboard * fix typo * Update readme_mtbench101.md * fit newest opencompass * update readme.md * mtbench101 to opencompass * mtbench101 to opencompass * for code review * for code review * for code review * hook * hook --------- Co-authored-by: liujie <ljie@buaa.edu.cn>
95 lines
3.0 KiB
Python
95 lines
3.0 KiB
Python
from mmengine.config import read_base
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with read_base():
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from .datasets.subjective.multiround.mtbench101_judge 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 MTBench101Summarizer
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# ---------------------------------------------------------------------------------------------------------
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api_meta_template = dict(
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round=[
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dict(role='SYSTEM', api_role='SYSTEM'),
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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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# -------------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=4096,
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max_seq_len=4096,
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batch_size=1,
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run_cfg=dict(num_gpus=2, num_procs=1),
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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=SizePartitioner, max_task_size=10000),
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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=32,
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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_models = [dict(
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abbr='GPT4-Turbo',
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type=OpenAI,
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path='gpt-4-1106-preview', # To compare with the official leaderboard, please use 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=16,
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max_out_len=4096,
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max_seq_len=4096,
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batch_size=8,
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temperature=0.8,
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)]
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## ------------- Evaluation Configuration
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eval = dict(
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partitioner=dict(type=SubjectiveSizePartitioner, max_task_size=100000, mode='singlescore', models=models, judge_models=judge_models),
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runner=dict(type=LocalRunner, max_num_workers=32, task=dict(type=SubjectiveEvalTask)),
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)
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summarizer = dict(type=MTBench101Summarizer, judge_type='single')
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work_dir = 'outputs/mtbench101/'
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