wehub-resource-sync
61 lines
1.8 KiB
Python
61 lines
1.8 KiB
Python
import os
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os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
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os.environ['ASCEND_RT_VISIBLE_DEVICES'] = '0,1'
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def test_embedding():
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from swift.megatron import MegatronSftArguments, megatron_sft_main
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megatron_sft_main(
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MegatronSftArguments(
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model='Qwen/Qwen3-Embedding-0.6B',
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task_type='embedding',
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dataset=['sentence-transformers/stsb:positive'],
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split_dataset_ratio=0.01,
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micro_batch_size=4,
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tensor_model_parallel_size=2,
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tuner_type='lora',
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num_train_epochs=1,
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recompute_granularity='full',
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recompute_method='uniform',
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recompute_num_layers=1,
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loss_type='infonce',
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vit_attn_impl='flash_attn',
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max_length=2048,
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eval_iters=5,
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save_steps=5,
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no_save_optim=True,
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no_save_rng=True,
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sequence_parallel=True,
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finetune=True))
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def test_reranker():
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from swift.megatron import MegatronSftArguments, megatron_sft_main
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megatron_sft_main(
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MegatronSftArguments(
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model='Qwen/Qwen3-Reranker-4B',
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tuner_type='lora',
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load_from_cache_file=True,
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num_train_epochs=1,
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task_type='generative_reranker',
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dataset=['MTEB/scidocs-reranking#2000'],
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loss_type='pointwise_reranker',
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split_dataset_ratio=0.01,
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tensor_model_parallel_size=2,
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recompute_granularity='full',
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recompute_method='uniform',
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recompute_num_layers=1,
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train_iters=100,
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eval_iters=5,
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save_steps=5,
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no_save_optim=True,
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no_save_rng=True,
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sequence_parallel=True,
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finetune=True))
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if __name__ == '__main__':
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test_embedding()
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# test_reranker()
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