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# This config is used for pass@k evaluation with dataset repetition
# That model cannot generate multiple response for single input
from mmengine.config import read_base
from opencompass.partitioners import SizePartitioner
from opencompass.models import HuggingFaceCausalLM
from opencompass.runners import LocalRunner
from opencompass.partitioners import SizePartitioner
from opencompass.tasks import OpenICLInferTask
with read_base():
from .datasets.humaneval.humaneval_repeat10_gen_8e312c import humaneval_datasets
from .datasets.mbpp.mbpp_repeat10_gen_1e1056 import mbpp_datasets
from .datasets.mbpp.sanitized_mbpp_repeat10_gen_1e1056 import sanitized_mbpp_datasets
datasets = []
datasets += humaneval_datasets
datasets += mbpp_datasets
datasets += sanitized_mbpp_datasets
_meta_template = dict(
round=[
dict(role="HUMAN", begin="<|User|>:", end="\n"),
dict(role="BOT", begin="<|Bot|>:", end="<eoa>\n", generate=True),
],
)
models = [
dict(
abbr="internlm-chat-7b-hf-v11",
type=HuggingFaceCausalLM,
path="internlm/internlm-chat-7b-v1_1",
tokenizer_path="internlm/internlm-chat-7b-v1_1",
tokenizer_kwargs=dict(
padding_side="left",
truncation_side="left",
use_fast=False,
trust_remote_code=True,
),
max_seq_len=2048,
meta_template=_meta_template,
model_kwargs=dict(trust_remote_code=True, device_map="auto"),
generation_kwargs=dict(
do_sample=True,
top_p=0.95,
temperature=0.8,
),
run_cfg=dict(num_gpus=1, num_procs=1),
batch_size=8,
)
]
infer = dict(
partitioner=dict(type=SizePartitioner, max_task_size=600),
runner=dict(
type=LocalRunner, max_num_workers=16,
task=dict(type=OpenICLInferTask)),
)
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