| # LoRA Training |
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| Best practice reference for single-node 8xH20 LoRA training with Qwen3-235B-A22B-Instruct-250718: https://github.com/modelscope/ms-swift/pull/5033. |
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| For environment setup, please refer to the [Quick Start Guide](./Quick-start.md) of Megatron-SWIFT. |
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| ## Converting HF to Mcore |
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| The conversion process is the same as for full-parameter training. Use the following script: |
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| ```shell |
| CUDA_VISIBLE_DEVICES=0 \ |
| swift export \ |
| --model Qwen/Qwen2.5-7B-Instruct \ |
| --to_mcore true \ |
| --torch_dtype bfloat16 \ |
| --output_dir Qwen2.5-7B-Instruct-mcore \ |
| --test_convert_precision true |
| ``` |
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| ## LoRA Training |
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| Training Script: |
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| ```bash |
| # full: 2 * 70GiB 0.61s/it |
| # lora: 2 * 14GiB 0.45s/it |
| PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \ |
| NPROC_PER_NODE=2 \ |
| CUDA_VISIBLE_DEVICES=0,1 \ |
| megatron sft \ |
| --load Qwen2.5-7B-Instruct-mcore \ |
| --dataset 'AI-ModelScope/alpaca-gpt4-data-zh#500' \ |
| 'AI-ModelScope/alpaca-gpt4-data-en#500' \ |
| 'swift/self-cognition#500' \ |
| --train_type lora \ |
| --lora_rank 8 \ |
| --lora_alpha 32 \ |
| --target_modules all-linear \ |
| --tensor_model_parallel_size 2 \ |
| --sequence_parallel true \ |
| --micro_batch_size 16 \ |
| --global_batch_size 16 \ |
| --recompute_granularity full \ |
| --recompute_method uniform \ |
| --recompute_num_layers 1 \ |
| --finetune true \ |
| --cross_entropy_loss_fusion true \ |
| --lr 1e-4 \ |
| --lr_warmup_fraction 0.05 \ |
| --min_lr 1e-5 \ |
| --max_epochs 1 \ |
| --save megatron_output/Qwen2.5-7B-Instruct \ |
| --save_interval 100 \ |
| --max_length 2048 \ |
| --system 'You are a helpful assistant.' \ |
| --num_workers 4 \ |
| --no_save_optim true \ |
| --no_save_rng true \ |
| --dataset_num_proc 4 \ |
| --model_author swift \ |
| --model_name swift-robot |
| ``` |
| - For LoRA training scripts of MoE models, please refer to [here](https://github.com/modelscope/ms-swift/tree/main/examples/megatron/lora). |
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| ## Converting MCore to HF |
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| ```bash |
| CUDA_VISIBLE_DEVICES=0 \ |
| swift export \ |
| --mcore_adapters megatron_output/Qwen2.5-7B-Instruct/vx-xxx \ |
| --to_hf true \ |
| --torch_dtype bfloat16 \ |
| --output_dir megatron_output/Qwen2.5-7B-Instruct/vx-xxx-hf \ |
| --test_convert_precision true |
| ``` |
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| - Note: The `mcore_adapters` folder contains an `args.json` file. During the conversion process, parameters related to `mcore_model` and LoRA will be loaded from this file. The system will then perform a merge-lora operation between the `mcore_model` and `mcore_adapters` to obtain the complete model weights, and finally convert them into HuggingFace (HF) format. (Conversion of LoRA incremental weights is not supported for now) |
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| ## Merge-LoRA |
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| If you only want to merge the LoRA weights without converting them to Hugging Face format, for subsequent DPO training, you can use the following script: |
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| ```shell |
| CUDA_VISIBLE_DEVICES=0 \ |
| swift export \ |
| --mcore_adapters megatron_output/Qwen2.5-7B-Instruct/vx-xxx \ |
| --to_mcore true \ |
| --torch_dtype bfloat16 \ |
| --output_dir megatron_output/Qwen2.5-7B-Instruct/vx-xxx-mcore \ |
| --test_convert_precision true |
| ``` |
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