diff --git a/docs/source/multi_gpu.rst b/docs/source/multi_gpu.rst
index b31490e000..706a977290 100644
--- a/docs/source/multi_gpu.rst
+++ b/docs/source/multi_gpu.rst
@@ -67,7 +67,7 @@ register the tensor as a buffer in your modules's `__init__` method with :meth:`
 
 Remove samplers
 ^^^^^^^^^^^^^^^
-In PyTorch, you must use `torch.nn.DistributedSampler` for multi-node or TPU training in PyTorch. The
+In PyTorch, you must use `torch.nn.DistributedSampler` for multi-node or TPU training. The
 sampler makes sure each GPU sees the appropriate part of your data.
 
 .. testcode::
diff --git a/pytorch_lightning/core/lightning.py b/pytorch_lightning/core/lightning.py
index d11c8ed9e7..d23cde63f4 100644
--- a/pytorch_lightning/core/lightning.py
+++ b/pytorch_lightning/core/lightning.py
@@ -1670,7 +1670,7 @@ class LightningModule(ABC, DeviceDtypeModuleMixin, GradInformation, ModelIO, Mod
         >>> class ManuallyArgsModel(LightningModule):
         ...     def __init__(self, arg1, arg2, arg3):
         ...         super().__init__()
-        ...         # manually assine arguments
+        ...         # manually assign arguments
         ...         self.save_hyperparameters('arg1', 'arg3')
         ...     def forward(self, *args, **kwargs):
         ...         ...