import os os.environ['CUDA_VISIBLE_DEVICES'] = '0,1' def test_sft(): from swift.megatron import MegatronSftArguments, megatron_sft_main megatron_sft_main( MegatronSftArguments( mcore_model='Qwen2.5-3B-Instruct-mcore', dataset=['AI-ModelScope/function-calling-chatml#10000'], loss_scale='hermes', split_dataset_ratio=0.01, tensor_model_parallel_size=2, tuner_type='lora', recompute_granularity='full', recompute_method='uniform', recompute_num_layers=1, # pipeline_model_parallel_size=2, # freeze_parameters_ratio=0.5, train_iters=100, modules_to_save=['word_embeddings', 'output_layer'], eval_iters=5, save_steps=5, no_save_optim=True, no_save_rng=True, sequence_parallel=True, finetune=True)) def test_moe(): from swift.megatron import MegatronSftArguments, megatron_sft_main megatron_sft_main( MegatronSftArguments( mcore_model='Qwen1.5-MoE-A2.7B-mcore', dataset=['AI-ModelScope/alpaca-gpt4-data-zh#5000'], split_dataset_ratio=0.01, moe_shared_expert_overlap=True, moe_grouped_gemm=True, tensor_model_parallel_size=2, # expert_model_parallel_size=2, tuner_type='lora', recompute_granularity='full', modules_to_save=['word_embeddings', 'output_layer'], recompute_method='uniform', recompute_num_layers=1, # pipeline_model_parallel_size=2, # freeze_parameters_ratio=0.5, train_iters=100, eval_iters=5, save_steps=5, no_save_optim=True, no_save_rng=True, sequence_parallel=True, finetune=True)) def test_convert(): from swift import ExportArguments, export_main export_main( ExportArguments( mcore_adapter='megatron_output/vx-xxx/checkpoint-xxx', to_hf=True, test_convert_precision=True, )) def test_embedding(): pass def test_resume(): pass if __name__ == '__main__': test_sft() # test_moe() # test_convert()