[DOCS] minor MO fixes (#18603)

docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md

@@ -45,11 +45,11 @@ To convert a model to OpenVINO model format (``ov.Model``), you can use the foll
 If the out-of-the-box conversion (only the ``input_model`` parameter is specified) is not successful, use the parameters mentioned below to override input shapes and cut the model:
 
 - model conversion API provides two parameters to override original input shapes for model conversion: ``input`` and ``input_shape``.
-For more information about these parameters, refer to the :doc:`Setting Input Shapes <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>` guide.
+  For more information about these parameters, refer to the :doc:`Setting Input Shapes <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>` guide.
 
 - To cut off unwanted parts of a model (such as unsupported operations and training sub-graphs),
-use the ``input`` and ``output`` parameters to define new inputs and outputs of the converted model.
-For a more detailed description, refer to the :doc:`Cutting Off Parts of a Model <openvino_docs_MO_DG_prepare_model_convert_model_Cutting_Model>` guide.
+  use the ``input`` and ``output`` parameters to define new inputs and outputs of the converted model.
+  For a more detailed description, refer to the :doc:`Cutting Off Parts of a Model <openvino_docs_MO_DG_prepare_model_convert_model_Cutting_Model>` guide.
 
 You can also insert additional input pre-processing sub-graphs into the converted model by using
 the ``mean_values``, ``scales_values``, ``layout``, and other parameters described

docs/MO_DG/prepare_model/Additional_Optimizations.md

@@ -5,9 +5,9 @@
 Input data for inference can be different from the training dataset and requires
 additional preprocessing before inference. To accelerate the whole pipeline including
 preprocessing and inference, model conversion API provides special parameters such as ``mean_values``,
+``scale_values``, ``reverse_input_channels``, and ``layout``. 
 
-``scale_values``, ``reverse_input_channels``, and ``layout``. Based on these
-parameters, model conversion API generates OpenVINO IR with additionally inserted sub-graphs
+Based on these parameters, model conversion API generates OpenVINO IR with additionally inserted sub-graphs
 to perform the defined preprocessing. This preprocessing block can perform mean-scale
 normalization of input data, reverting data along channel dimension, and changing
 the data layout. See the following sections for details on the parameters, or the

docs/MO_DG/prepare_model/FP16_Compression.md

@@ -2,9 +2,9 @@
 
 @sphinxdirective
 
-Optionally all relevant floating-point weights can be compressed to ``FP16`` data type during the model conversion.
+Optionally, all relevant floating-point weights can be compressed to ``FP16`` data type during model conversion.
 It results in creating a "compressed ``FP16`` model", which occupies about half of 
-the original space in the file system. The compression may introduce a drop in accuracy.
+the original space in the file system. The compression may introduce a minor drop in accuracy,
 but it is negligible for most models.
 
 To compress the model, use the ``compress_to_fp16=True`` option:
@@ -18,14 +18,14 @@ To compress the model, use the ``compress_to_fp16=True`` option:
           :force:
 
           from openvino.tools.mo import convert_model
-          ov_model = convert_model(INPUT_MODEL, compress_to_fp16=False)
+          ov_model = convert_model(INPUT_MODEL, compress_to_fp16=True)
 
     .. tab-item:: CLI
        :sync: cli
 
        .. code-block:: sh
 
-          mo --input_model INPUT_MODEL --compress_to_fp16=False
+          mo --input_model INPUT_MODEL --compress_to_fp16=True
 
 
 For details on how plugins handle compressed ``FP16`` models, see 
@@ -40,8 +40,8 @@ For details on how plugins handle compressed ``FP16`` models, see
 
 .. note::
 
-   Some large models (larger than a few GB) when compressed to ``FP16`` may consume enormous amount of RAM on the loading
-   phase of the inference. In case if you are facing such problems, please try to convert them without compression: 
+   Some large models (larger than a few GB) when compressed to ``FP16`` may consume an overly large amount of RAM on the loading
+   phase of the inference. If that is the case for your model, try to convert it without compression: 
    ``convert_model(INPUT_MODEL, compress_to_fp16=False)`` or ``convert_model(INPUT_MODEL)``