当前位置：首页 > news >正文

使用MS-SWIF框架对大模型进行SFT微调

news 2025/9/6 10:24:08

MS-SWIFT训练框架
Qwen3最佳实践

GPU资源：Tesla V100S x4（32G x 4）
在这里插入图片描述

底座模型：Qwen3-1.7B
SFT数据集：
alpaca-gpt4-data-en
alpaca-gpt4-data-zh
自我认知微调数据集

训练

export CUDA_VISIBLE_DEVICES=0,1,2,3

swift sft
–model Qwen/Qwen3-1.7B
–train_type lora
–dataset ‘AI-ModelScope/alpaca-gpt4-data-zh#500’
‘AI-ModelScope/alpaca-gpt4-data-en#500’
‘swift/self-cognition#500’
–torch_dtype bfloat16
–num_train_epochs 1
–per_device_train_batch_size 1
–per_device_eval_batch_size 1
–learning_rate 1e-4
–lora_rank 8
–lora_alpha 32
–target_modules all-linear
–gradient_accumulation_steps 16
–eval_steps 50
–save_steps 50
–save_total_limit 2
–logging_steps 5
–max_length 2048
–output_dir output
–system ‘You are a helpful assistant.’
–warmup_ratio 0.05
–dataloader_num_workers 4
–model_author swift
–model_name swift-robot

推理

swift infer
–adapters output/vx-xxx/checkpoint-xxx
–stream true
–temperature 0
–max_new_tokens 2048

swift infer
–adapters /workspace/codes/oss/ms-swift/output/v3-20250905-012245/checkpoint-94
–stream true
–temperature 0
–max_new_tokens 2048

在这里插入图片描述

merge-lora并使用vLLM进行推理加速

swift infer
–adapters output/vx-xxx/checkpoint-xxx
–stream true
–merge_lora true
–infer_backend vllm
–max_model_len 8192
–temperature 0
–max_new_tokens 2048

swift infer
–adapters /workspace/codes/oss/ms-swift/output/v3-20250905-012245/checkpoint-94
–stream true
–merge_lora true
–infer_backend vllm
–max_model_len 8192
–temperature 0
–max_new_tokens 2048

报错：ValueError: Bfloat16 is only supported on GPUs with compute capability of at least 8.0. Your Tesla V100S-PCIE-32GB GPU has compute capability 7.0. You can use float16 instead by explicitly setting the dtype flag in CLI, for example: --dtype=half.
原因：用 Bfloat16（Brain Floating Point 16）数据类型来节省显存、加速训练，需要GPU 计算能力 ≥ 8.0

使用SGlang推理

SGLANG_USE_MODELSCOPE=true python -m sglang.launch_server --model-path /workspace/codes/oss/ms-swift/output/v3-20250905-012245/checkpoint-94-merged --reasoning-parser qwen3

报错：RuntimeError: SGLang only supports sm75 and above.
原因：SGLang 只支持计算能力（Compute Capability）为 sm_75 及以上的 GPU，你的 GPU 的计算能力低于 sm_75，因此无法运行

模型发布

swift export
–adapters /workspace/codes/oss/ms-swift/output/v3-20250905-012245/checkpoint-94
–push_to_hub true
–hub_model_id ‘lzhawesome/my-lora-model-Qwen3-1.7B’
–hub_token ‘xxxxx’
–use_hf false