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Minor editorial fixes

Author: svekars

intermediate_source/pipelining_tutorial.rst

@@ -5,7 +5,7 @@ Introduction to Distributed Pipeline Parallelism
 .. note::
    |edit| View and edit this tutorial in `github <https://github.com/pytorch/tutorials/blob/main/intermediate_source/pipelining_tutorial.rst>`__.
 
-This tutorial uses a gpt-style transformer model to demonstrate implementing distributed
+This tutorial uses a GPT-style transformer model to demonstrate implementing distributed
 pipeline parallelism with `torch.distributed.pipelining <https://pytorch.org/docs/main/distributed.pipelining.html>`__
 APIs.
 
@@ -25,7 +25,7 @@ APIs.
 Setup
 -----
 
-With ``torch.distributed.pipelining`` we will be partitioning the execution of a model and scheduling computation on micro-batches. We will be using a simplified version
+With ``torch.distributed.pipelining`` we will be partitioning the execution of a model and scheduling computation on microbatches. We will be using a simplified version
 of a transformer decoder model. The model architecture is for educational purposes and has multiple transformer decoder layers as we want to demonstrate how to split the model into different
 chunks. First, let us define the model:
 
@@ -218,7 +218,9 @@ Finally, we are ready to run the script. We will use ``torchrun`` to create a si
 Our script is already written in a way rank 0 that performs the required logic for pipeline stage 0, and rank 1
 performs the logic for pipeline stage 1.
 
-``torchrun --standalone --nnodes 1 --nproc_per_node 2 pipelining_tutorial.py``
+.. code-block:: bash
+
+   torchrun --standalone --nnodes 1 --nproc_per_node 2 pipelining_tutorial.py
 
 Conclusion
 ----------