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96 lines
3.0 KiB
Python
96 lines
3.0 KiB
Python
from mmengine.config import read_base
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with read_base():
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from .models.qwen.hf_qwen_7b_chat import models as hf_qwen_7b_chat
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from .models.qwen.hf_qwen_14b_chat import models as hf_qwen_14b_chat
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from .models.chatglm.hf_chatglm3_6b import models as hf_chatglm3_6b
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from .models.baichuan.hf_baichuan2_7b_chat import models as hf_baichuan2_7b
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from .models.hf_internlm.hf_internlm_chat_7b import models as hf_internlm_chat_7b
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from .models.hf_internlm.hf_internlm_chat_20b import models as hf_internlm_chat_20b
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from .datasets.subjective_cmp.subjective_creation import subjective_datasets
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datasets = [*subjective_datasets]
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from opencompass.models import HuggingFaceCausalLM, HuggingFace, OpenAI
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from opencompass.partitioners import NaivePartitioner
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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.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 Creationv01Summarizer
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models = [*hf_baichuan2_7b, *hf_chatglm3_6b, *hf_internlm_chat_20b, *hf_qwen_7b_chat, *hf_internlm_chat_7b, *hf_qwen_14b_chat]
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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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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='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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_meta_template = dict(
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round=[
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dict(role="HUMAN", begin='\n<|im_start|>user\n', end='<|im_end|>'),
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dict(role="BOT", begin="\n<|im_start|>assistant\n", end='<|im_end|>', generate=True),
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],
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)
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judge_model = dict(
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type=HuggingFaceCausalLM,
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abbr='qwen-7b-chat-hf',
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path="Qwen/Qwen-7B-Chat",
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tokenizer_path='Qwen/Qwen-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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pad_token_id=151643,
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max_out_len=2048,
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max_seq_len=2048,
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batch_size=8,
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meta_template=_meta_template,
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run_cfg=dict(num_gpus=1, num_procs=1),
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)
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eval = dict(
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partitioner=dict(
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type=SubjectiveNaivePartitioner,
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mode='singlescore',
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models = [*hf_baichuan2_7b]
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),
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runner=dict(
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type=SlurmSequentialRunner,
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partition='llmeval',
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quotatype='auto',
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max_num_workers=256,
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task=dict(
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type=SubjectiveEvalTask,
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judge_cfg=judge_model
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)),
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)
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work_dir = './creation/'
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summarizer = dict(
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type=Creationv01Summarizer,
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match_method='smart',
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) |