OpenCompass/examples/eval_code_passk.py
Linchen Xiao a6193b4c02
[Refactor] Code refactoarization (#1831)
* Update

* fix lint

* update

* fix lint
2025-01-20 19:17:38 +08:00

54 lines
1.7 KiB
Python

# This config is used for pass@k evaluation with `num_return_sequences`
# That model can generate multiple responses for single input
from mmengine.config import read_base
from opencompass.models import HuggingFaceCausalLM
from opencompass.partitioners import SizePartitioner
from opencompass.runners import LocalRunner
from opencompass.tasks import OpenICLInferTask
with read_base():
from opencompass.configs.datasets.humaneval.humaneval_passk_gen_8e312c import \
humaneval_datasets
from opencompass.configs.datasets.mbpp.deprecated_mbpp_passk_gen_1e1056 import \
mbpp_datasets
from opencompass.configs.datasets.mbpp.deprecated_sanitized_mbpp_passk_gen_1e1056 import \
sanitized_mbpp_datasets
datasets = []
datasets += humaneval_datasets
datasets += mbpp_datasets
datasets += sanitized_mbpp_datasets
models = [
dict(
type=HuggingFaceCausalLM,
abbr='CodeLlama-7b-Python',
path='codellama/CodeLlama-7b-Python-hf',
tokenizer_path='codellama/CodeLlama-7b-Python-hf',
tokenizer_kwargs=dict(
padding_side='left',
truncation_side='left',
trust_remote_code=True,
),
max_out_len=1024,
max_seq_len=2048,
batch_size=8,
model_kwargs=dict(trust_remote_code=True, device_map='auto'),
generation_kwargs=dict(
num_return_sequences=10,
do_sample=True,
top_p=0.95,
temperature=0.8,
),
run_cfg=dict(num_gpus=1, num_procs=1),
),
]
infer = dict(
partitioner=dict(type=SizePartitioner, max_task_size=300),
runner=dict(type=LocalRunner,
max_num_workers=16,
task=dict(type=OpenICLInferTask)),
)