| import os
|
|
|
| os.environ['CUDA_VISIBLE_DEVICES'] = '0'
|
|
|
| kwargs = {
|
| 'per_device_train_batch_size': 4,
|
| 'save_steps': 5,
|
| 'gradient_accumulation_steps': 4,
|
| 'num_train_epochs': 1,
|
| }
|
|
|
|
|
| def test_embedding():
|
| from swift import sft_main, SftArguments
|
| result = sft_main(
|
| SftArguments(
|
| model='Qwen/Qwen3-Embedding-0.6B',
|
| task_type='embedding',
|
| dataset=['sentence-transformers/stsb:positive'],
|
| split_dataset_ratio=0.01,
|
| load_from_cache_file=False,
|
| loss_type='infonce',
|
| attn_impl='flash_attn',
|
| max_length=2048,
|
| **kwargs,
|
| ))
|
| last_model_checkpoint = result['last_model_checkpoint']
|
| print(f'last_model_checkpoint: {last_model_checkpoint}')
|
|
|
|
|
| def test_reranker():
|
| from swift import sft_main, SftArguments
|
| result = sft_main(
|
| SftArguments(
|
| model='Qwen/Qwen3-Reranker-4B',
|
| tuner_type='lora',
|
| load_from_cache_file=True,
|
| task_type='generative_reranker',
|
| dataset=['MTEB/scidocs-reranking#10000'],
|
| split_dataset_ratio=0.05,
|
| loss_type='pointwise_reranker',
|
| dataloader_drop_last=True,
|
| eval_strategy='steps',
|
| eval_steps=10,
|
| max_length=4096,
|
| attn_impl='flash_attn',
|
| num_train_epochs=1,
|
| save_steps=200,
|
| per_device_train_batch_size=2,
|
| per_device_eval_batch_size=2,
|
| gradient_accumulation_steps=8,
|
| dataset_num_proc=2,
|
| ))
|
| last_model_checkpoint = result['last_model_checkpoint']
|
| print(f'last_model_checkpoint: {last_model_checkpoint}')
|
|
|
|
|
| def test_reranker2():
|
| from swift import sft_main, SftArguments
|
| result = sft_main(
|
| SftArguments(
|
| model='Qwen/Qwen2.5-VL-3B-Instruct',
|
| tuner_type='lora',
|
| load_from_cache_file=True,
|
| task_type='reranker',
|
| dataset=['MTEB/scidocs-reranking'],
|
| split_dataset_ratio=0.05,
|
| loss_type='listwise_reranker',
|
| dataloader_drop_last=True,
|
| eval_strategy='steps',
|
| eval_steps=10,
|
| max_length=4096,
|
| attn_impl='flash_attn',
|
| padding_side='right',
|
| num_train_epochs=1,
|
| save_steps=200,
|
| per_device_train_batch_size=2,
|
| per_device_eval_batch_size=2,
|
| gradient_accumulation_steps=8,
|
| dataset_num_proc=1,
|
| ))
|
| last_model_checkpoint = result['last_model_checkpoint']
|
| print(f'last_model_checkpoint: {last_model_checkpoint}')
|
|
|
|
|
| if __name__ == '__main__':
|
|
|
| test_reranker()
|
|
|
