60 lines
2.2 KiB
Python
60 lines
2.2 KiB
Python
import os
|
|
from pprint import pprint
|
|
|
|
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
|
|
os.environ['ASCEND_RT_VISIBLE_DEVICES'] = '0'
|
|
kwargs = {
|
|
'per_device_train_batch_size': 4,
|
|
'per_device_eval_batch_size': 4,
|
|
'gradient_accumulation_steps': 4,
|
|
'num_train_epochs': 1,
|
|
'save_steps': 100,
|
|
'max_length': 512,
|
|
'task_type': 'seq_cls',
|
|
'num_labels': 2,
|
|
}
|
|
|
|
|
|
def calc_acc(infer_result):
|
|
n_correct = 0
|
|
for res in infer_result:
|
|
if res['response'] == res['labels']:
|
|
n_correct += 1
|
|
return f'acc: {n_correct / len(infer_result)}, n_correct: {n_correct}, len(res): {len(infer_result)}'
|
|
|
|
|
|
def test_llm():
|
|
from swift import InferArguments, SftArguments, infer_main, sft_main
|
|
res = []
|
|
for model in ['Qwen/Qwen2.5-0.5B-Instruct', 'Qwen/Qwen2.5-0.5B', 'AI-ModelScope/bert-base-chinese']:
|
|
dataset = ['DAMO_NLP/jd:cls#2000']
|
|
result = sft_main(SftArguments(model=model, dataset=dataset, split_dataset_ratio=0.1, **kwargs))
|
|
last_model_checkpoint = result['last_model_checkpoint']
|
|
infer_result = infer_main(
|
|
InferArguments(adapters=[last_model_checkpoint], load_data_args=True, truncation_strategy='right'))
|
|
res.append(calc_acc(infer_result))
|
|
infer_result2 = infer_main(
|
|
InferArguments(
|
|
adapters=[last_model_checkpoint], load_data_args=True, max_batch_size=16, truncation_strategy='right'))
|
|
res.append(calc_acc(infer_result2))
|
|
|
|
model = 'Qwen/Qwen2.5-0.5B-Instruct'
|
|
dataset = ['DAMO_NLP/jd#2000']
|
|
train_kwargs = kwargs.copy()
|
|
train_kwargs.pop('task_type')
|
|
train_kwargs.pop('num_labels')
|
|
result = sft_main(SftArguments(model=model, dataset=dataset, split_dataset_ratio=0.1, **train_kwargs))
|
|
last_model_checkpoint = result['last_model_checkpoint']
|
|
infer_result = infer_main(
|
|
InferArguments(adapters=[last_model_checkpoint], load_data_args=True, truncation_strategy='right'))
|
|
res.append(calc_acc(infer_result))
|
|
infer_result2 = infer_main(
|
|
InferArguments(
|
|
adapters=[last_model_checkpoint], load_data_args=True, max_batch_size=16, truncation_strategy='right'))
|
|
res.append(calc_acc(infer_result2))
|
|
pprint(res)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_llm()
|