OpenCompass/configs/eval_subjective_judge_pandalm.py
bittersweet1999 97c2068bd9
[Feature] Add JudgeLLMs (#710)
* add judgellms

* add judgellms

* add sub_size_partition

* add docs

* add ref
2023-12-19 18:40:25 +08:00

85 lines
2.7 KiB
Python

from mmengine.config import read_base
with read_base():
from .models.qwen.hf_qwen_7b_chat import models as hf_qwen_7b_chat
from .models.qwen.hf_qwen_14b_chat import models as hf_qwen_14b_chat
from .models.chatglm.hf_chatglm3_6b import models as hf_chatglm3_6b
from .models.baichuan.hf_baichuan2_7b_chat import models as hf_baichuan2_7b
from .models.hf_internlm.hf_internlm_chat_20b import models as hf_internlm_chat_20b
from .datasets.subjective_cmp.alignment_bench import subjective_datasets
datasets = [*subjective_datasets]
from opencompass.models import HuggingFaceCausalLM, HuggingFace, OpenAIAllesAPIN, HuggingFaceChatGLM3
from opencompass.partitioners import NaivePartitioner
from opencompass.partitioners.sub_naive import SubjectiveNaivePartitioner
from opencompass.runners import LocalRunner
from opencompass.runners import SlurmSequentialRunner
from opencompass.tasks import OpenICLInferTask
from opencompass.tasks.subjective_eval import SubjectiveEvalTask
from opencompass.summarizers import AlignmentBenchSummarizer
# -------------Inferen Stage ----------------------------------------
models = [*hf_baichuan2_7b]#, *hf_chatglm3_6b, *hf_internlm_chat_20b, *hf_qwen_7b_chat, *hf_qwen_14b_chat]
infer = dict(
partitioner=dict(type=NaivePartitioner),
runner=dict(
type=SlurmSequentialRunner,
partition='llmeval',
quotatype='auto',
max_num_workers=256,
task=dict(type=OpenICLInferTask)),
)
# -------------Evalation Stage ----------------------------------------
## ------------- JudgeLLM Configuration
api_meta_template = dict(
round=[
dict(role='HUMAN', api_role='HUMAN'),
dict(role='BOT', api_role='BOT', generate=True),
]
)
judge_model = dict(
type=HuggingFaceCausalLM,
abbr='pandalm-7b-v1-hf',
path="WeOpenML/PandaLM-7B-v1",
tokenizer_path='WeOpenML/PandaLM-7B-v1',
tokenizer_kwargs=dict(padding_side='left',
truncation_side='left',
trust_remote_code=True,
use_fast=False,),
max_out_len=512,
max_seq_len=2048,
batch_size=8,
model_kwargs=dict(device_map='auto', trust_remote_code=True),
run_cfg=dict(num_gpus=1, num_procs=1),
)
## ------------- Evaluation Configuration
eval = dict(
partitioner=dict(
type=SubjectiveNaivePartitioner,
mode='singlescore',
models = [*hf_baichuan2_7b]
),
runner=dict(
type=LocalRunner,
max_num_workers=2,
task=dict(
type=SubjectiveEvalTask,
judge_cfg=judge_model
)),
)
summarizer = dict(
type=AlignmentBenchSummarizer,
)
work_dir = 'outputs/pandalm'