mirror of
https://github.com/open-compass/opencompass.git
synced 2025-05-30 16:03:24 +08:00
79 lines
2.8 KiB
Python
79 lines
2.8 KiB
Python
|
from opencompass.models import HuggingFaceCausalLM
|
||
|
|
from opencompass.models.turbomind import TurboMindModel
|
||
|
|
from opencompass.runners import SlurmSequentialRunner
|
||
|
|
from opencompass.partitioners import SizePartitioner, NaivePartitioner
|
||
|
|
from opencompass.tasks import OpenICLInferTask, OpenICLEvalTask
|
||
|
|
|
||
|
|
from mmengine.config import read_base
|
||
|
|
with read_base():
|
||
|
|
# eval needlebench_4k
|
||
|
|
from .datasets.needlebench.needlebench_4k.needlebench import needlebench_datasets
|
||
|
|
from .summarizers.needlebench import needlebench_4k_summarizer as summarizer
|
||
|
|
|
||
|
|
# only eval original "needle in a haystack test" in needlebench_4k
|
||
|
|
# from .datasets.needlebench.needlebench_4k.needlebench_single import needlebench_datasets_zh, needlebench_datasets_en
|
||
|
|
# from .summarizers.needlebench import needlebench_4k_summarizer as summarizer
|
||
|
|
|
||
|
|
# eval Ancestral Tracing Challenge(ATC)
|
||
|
|
# from .datasets.needlebench.atc.atc import needlebench_atc_datasets_zh, needlebench_atc_datasets_en
|
||
|
|
# from .summarizers.needlebench import needlebench_atc_summarizer as summarizer
|
||
|
|
|
||
|
|
datasets = sum([v for k, v in locals().items() if ('datasets' in k)], [])
|
||
|
|
|
||
|
|
hf_internlm2_chat_7b_model_meta_template = dict(
|
||
|
|
round=[
|
||
|
|
dict(role='HUMAN',
|
||
|
|
begin='<|im_start|>user\n', end='<|im_end|>\n'),
|
||
|
|
dict(role='BOT', begin='<|im_start|>assistant\n',
|
||
|
|
end='<|im_end|>\n', generate=True),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
hf_internlm2_chat_7b = dict(
|
||
|
|
type=HuggingFaceCausalLM,
|
||
|
|
abbr='internlm2-chat-7b-hf',
|
||
|
|
path="internlm/internlm2-chat-7b",
|
||
|
|
tokenizer_path='internlm/internlm2-chat-7b',
|
||
|
|
model_kwargs=dict(
|
||
|
|
trust_remote_code=True,
|
||
|
|
device_map='auto',
|
||
|
|
),
|
||
|
|
tokenizer_kwargs=dict(
|
||
|
|
padding_side='left',
|
||
|
|
truncation_side='left',
|
||
|
|
use_fast=False,
|
||
|
|
trust_remote_code=True,
|
||
|
|
),
|
||
|
|
max_out_len=2000,
|
||
|
|
max_seq_len=32768,
|
||
|
|
batch_size=8,
|
||
|
|
meta_template=hf_internlm2_chat_7b_model_meta_template,
|
||
|
|
run_cfg=dict(num_gpus=1, num_procs=1),
|
||
|
|
end_str='<|im_end|>',
|
||
|
|
)
|
||
|
|
|
||
|
|
internlm2_chat_7b_200k = dict(
|
||
|
|
type=TurboMindModel,
|
||
|
|
abbr='internlm2-chat-7b-200k',
|
||
|
|
path="internlm/internlm2-chat-7b",
|
||
|
|
meta_template=hf_internlm2_chat_7b_model_meta_template,
|
||
|
|
engine_config=dict(session_len=210000,
|
||
|
|
max_batch_size=8,
|
||
|
|
rope_scaling_factor=2.0,
|
||
|
|
model_name="internlm2-chat-7b"),
|
||
|
|
gen_config=dict(top_k=1, top_p=0.8,
|
||
|
|
temperature=1.0,
|
||
|
|
max_new_tokens=2000),
|
||
|
|
max_out_len=2000,
|
||
|
|
max_seq_len=210000,
|
||
|
|
batch_size=8,
|
||
|
|
concurrency=8,
|
||
|
|
run_cfg=dict(num_gpus=1, num_procs=1),
|
||
|
|
)
|
||
|
|
|
||
|
|
models = [
|
||
|
|
# hf_internlm2_chat_7b,
|
||
|
|
internlm2_chat_7b_200k,
|
||
|
|
]
|
||
|
|
|
||
|
|
work_dir = './outputs/needlebench'
|