diff --git a/doc/gpu/index.rst b/doc/gpu/index.rst
index a11b753fe..468362302 100644
--- a/doc/gpu/index.rst
+++ b/doc/gpu/index.rst
@@ -61,7 +61,7 @@ The dataset itself is stored on device in a compressed ELLPACK format. The ELLPA
 
 Working memory is allocated inside the algorithm proportional to the number of rows to keep track of gradients, tree positions and other per row statistics. Memory is allocated for histogram bins proportional to the number of bins, number of features and nodes in the tree. For performance reasons we keep histograms in memory from previous nodes in the tree, when a certain threshold of memory usage is passed we stop doing this to conserve memory at some performance loss.
 
-If you are getting out-of-memory errors on a big dataset, try the or :py:class:`xgboost.QuantileDMatrix` or :doc:`external memory version </tutorials/external_memory>`. Note that when ``external memory`` is used for GPU hist, it's best to employ gradient based sampling as well. Last but not least, ``inplace_predict`` can be preferred over ``predict`` when data is already on GPU. Both ``QuantileDMatrix`` and ``inplace_predict`` are automatically enabled if you are using the scikit-learn interface.
+If you are getting out-of-memory errors on a big dataset, try the :py:class:`xgboost.QuantileDMatrix` or :doc:`external memory version </tutorials/external_memory>`. Note that when ``external memory`` is used for GPU hist, it's best to employ gradient based sampling as well. Last but not least, ``inplace_predict`` can be preferred over ``predict`` when data is already on GPU. Both ``QuantileDMatrix`` and ``inplace_predict`` are automatically enabled if you are using the scikit-learn interface.
 
 
 CPU-GPU Interoperability
diff --git a/python-package/xgboost/core.py b/python-package/xgboost/core.py
index 76251d65c..e8bc735e6 100644
--- a/python-package/xgboost/core.py
+++ b/python-package/xgboost/core.py
@@ -1486,7 +1486,7 @@ class QuantileDMatrix(DMatrix):
     by avoiding intermediate storage. Set ``max_bin`` to control the number of bins
     during quantisation, which should be consistent with the training parameter
     ``max_bin``. When ``QuantileDMatrix`` is used for validation/test dataset, ``ref``
-    should be another ``QuantileDMatrix``(or ``DMatrix``, but not recommended as it
+    should be another ``QuantileDMatrix`` or ``DMatrix``, but not recommended as it
     defeats the purpose of saving memory) constructed from training dataset.  See
     :py:obj:`xgboost.DMatrix` for documents on meta info.