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112 lines
3.1 KiB
Python
112 lines
3.1 KiB
Python
from opencompass.models import HuggingFaceCausalLM
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from copy import deepcopy
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from opencompass.models import TurboMindModel
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from mmengine.config import read_base
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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 MultiroundSummarizer
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with read_base():
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from .datasets.subjective.multiround.functionalmt_zh_judgeby_gpt4 import subjective_datasets
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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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_meta_template = dict(
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round=[
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dict(role='HUMAN', begin='<|im_start|>user\n', end='<|im_end|>\n'),
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dict(role='BOT', begin='<|im_start|>assistant\n', end='<|im_end|>\n', generate=True),
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],
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eos_token_id=151645,
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)
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models = [
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dict(
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type=HuggingFaceCausalLM,
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abbr='qwen1.5-7b-chat-hf',
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path='Qwen/Qwen1.5-7B-Chat',
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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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use_fast=False,
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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=_meta_template,
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pad_token_id=151645,
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max_out_len=100,
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max_seq_len=2048,
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batch_size=8,
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run_cfg=dict(num_gpus=1, num_procs=1),
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end_str='<|im_end|>',
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)
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]
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datasets = [*subjective_datasets]
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work_dir = 'outputs/multiround/'
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# -------------Inferen Stage ----------------------------------------
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infer = dict(
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partitioner=dict(type=SizePartitioner, max_task_size=1000),
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runner=dict(
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type=SlurmSequentialRunner,
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partition='your part',
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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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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='',
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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=10,
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retry=10,
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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,
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max_task_size=1000,
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mode='singlescore',
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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=SlurmSequentialRunner,
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partition='your part',
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quotatype='auto',
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max_num_workers=256,
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task=dict(type=SubjectiveEvalTask)),
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)
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summarizer = dict(
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type=MultiroundSummarizer
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)
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