File size: 1,678 Bytes
256a159 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
# 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
with read_base():
from .datasets.humaneval.humaneval_passk_gen_8e312c import humaneval_datasets
from .datasets.mbpp.mbpp_passk_gen_1e1056 import mbpp_datasets
from .datasets.mbpp.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)),
)
|