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1fe152b3e8
* alignmentbench infer and judge * alignmentbench * alignmentbench done * alignment all done * alignment all done
93 lines
2.8 KiB
Python
93 lines
2.8 KiB
Python
from os import getenv as gv
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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_20b import models as hf_internlm_chat_20b
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from .datasets.subjective_cmp.alignment_bench import subjective_datasets
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datasets = [*subjective_datasets]
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from opencompass.models import HuggingFaceCausalLM, HuggingFace, OpenAI, HuggingFaceChatGLM3
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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 AlignmentBenchSummarizer
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models = [*hf_baichuan2_7b]#, *hf_chatglm3_6b, *hf_internlm_chat_20b, *hf_qwen_7b_chat, *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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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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judge_model = 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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meta_template=api_meta_template,
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max_out_len=100,
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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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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 = gv('WORKDIR')+'alignment_bench/'
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summarizer = dict(
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type=AlignmentBenchSummarizer,
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) |