diff --git a/README.zh.md b/README.zh.md
index bdcd5e5ddc6990fd7da2260d039610a2f5ad7836..0e85e667a8cf814edd3e79f3342e1f431ba09aa0 100644
--- a/README.zh.md
+++ b/README.zh.md
@@ -58,7 +58,7 @@ Apex及Apex-patch的卸载只需执行命令：
 
 使用apex.amp进行混合精度训练：
 ```
-model = torch.nn.Linear(D_in, D_out).cuda()
+model = torch.nn.Linear(D_in, D_out).npu()
 optimzier = torch.optim.SGD(model.parameters(), lr=1e-3)
 
 model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
@@ -74,7 +74,7 @@ with amp.scale_loss(loss, optimizer) as scaled_loss:
 
 在amp.initialize()中将参数combine_grad设置为True，如：
 ```
-model = torch.nn.Linear(D_in, D_out).cuda()
+model = torch.nn.Linear(D_in, D_out).npu()
 optimzier = torch.optim.SGD(model.parameters(), lr=1e-3)
 
 model, optimizer = amp.initialize(model, optimizer, opt_level='O1', combine_grad=True)  # 增加combine_grad参数
@@ -88,7 +88,7 @@ with amp.scale_loss(loss, optimizer) as scaled_loss:
 
 将torch原生优化器torch.optim.xxx替换为apex.optimizers.xxx, 其中xxx为融合优化器名称，apex-patch支持的优化器见*特性介绍*。
 ```
-model = torch.nn.Linear(D_in, D_out).cuda()
+model = torch.nn.Linear(D_in, D_out).npu()
 optimzier = apex.optimizers.NpuFusedSGD(model.parameters(), lr=1e-3) # 使用apex.optimizers.NpuFusedSGD
 
 model, optimizer = amp.initialize(model, optimizer, opt_level='O1', combine_grad=True)