mirror of
https://github.com/open-compass/opencompass.git
synced 2025-05-30 16:03:24 +08:00
91fba2c2e9
* [Feat] support pass@ k * [Feat] support pass@k * [Feat] support pass@k * [Feat] support pass@k * [Feat] support pass@k * [Feat] support pass@k docs * update naming --------- Co-authored-by: Leymore <zfz-960727@163.com>
55 lines
1.7 KiB
Python
55 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.partitioners import SizePartitioner
|
|
from opencompass.models import HuggingFaceCausalLM
|
|
from opencompass.runners import LocalRunner
|
|
from opencompass.partitioners import SizePartitioner
|
|
from opencompass.tasks import OpenICLInferTask
|
|
from opencompass.datasets import MBPPDataset_V2, MBPPPassKEvaluator
|
|
|
|
with read_base():
|
|
from .datasets.humaneval.humaneval_gen_8e312c import humaneval_datasets
|
|
from .datasets.mbpp.mbpp_gen_1e1056 import mbpp_datasets
|
|
|
|
mbpp_datasets[0]['type'] = MBPPDataset_V2
|
|
mbpp_datasets[0]['eval_cfg']['evaluator']['type'] = MBPPPassKEvaluator
|
|
mbpp_datasets[0]['reader_cfg']['output_column'] = 'test_column'
|
|
|
|
datasets = []
|
|
datasets += humaneval_datasets
|
|
datasets += 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)),
|
|
)
|