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from mmengine.config import read_base
from opencompass.models import LmdeployPytorchModel
with read_base():
# choose a list of datasets
from .datasets.mmlu.mmlu_gen_a484b3 import mmlu_datasets
from .datasets.ceval.ceval_gen_5f30c7 import ceval_datasets
from .datasets.SuperGLUE_WiC.SuperGLUE_WiC_gen_d06864 import WiC_datasets
from .datasets.SuperGLUE_WSC.SuperGLUE_WSC_gen_7902a7 import WSC_datasets
from .datasets.triviaqa.triviaqa_gen_2121ce import triviaqa_datasets
from .datasets.gsm8k.gsm8k_gen_1d7fe4 import gsm8k_datasets
from .datasets.race.race_gen_69ee4f import race_datasets
from .datasets.crowspairs.crowspairs_gen_381af0 import crowspairs_datasets
# and output the results in a choosen format
from .summarizers.medium import summarizer
datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
meta_template = dict(
round=[
dict(role='HUMAN', begin='<|User|>:', end='<eoh>\n'),
dict(role='BOT', begin='<|Bot|>:', end='<eoa>\n', generate=True),
],
eos_token_id=103028)
# config for internlm-chat-7b
internlm_chat_7b = dict(
type=LmdeployPytorchModel,
abbr='internlm-chat-7b-pytorch',
path='internlm/internlm-chat-7b',
engine_config=dict(session_len=2048,
max_batch_size=16),
gen_config=dict(top_k=1,
top_p=0.8,
temperature=1.0,
max_new_tokens=100),
max_out_len=100,
max_seq_len=2048,
batch_size=16,
concurrency=16,
meta_template=meta_template,
run_cfg=dict(num_gpus=1, num_procs=1),
end_str='<eoa>',
)
# config for internlm-chat-20b
internlm_chat_20b = dict(
type=LmdeployPytorchModel,
abbr='internlm-chat-20b-pytorch',
path='internlm/internlm-chat-20b',
engine_config=dict(session_len=2048,
max_batch_size=8),
gen_config=dict(top_k=1,
top_p=0.8,
temperature=1.0,
max_new_tokens=100),
max_out_len=100,
max_seq_len=2048,
batch_size=8,
concurrency=8,
meta_template=meta_template,
run_cfg=dict(num_gpus=1, num_procs=1),
end_str='<eoa>',
)
models = [internlm_chat_20b]
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