Fixed technical issues with links

slyalin · Sep 4, 2023 · 08317aa · 08317aa
1 parent f2b2bc4
commit 08317aa
Show file tree

Hide file tree

Showing 3 changed files with 3 additions and 2 deletions.
diff --git a/docs/Documentation/model_introduction.md b/docs/Documentation/model_introduction.md
@@ -210,7 +210,7 @@ If a model is represented as a single file from ONNX, PaddlePaddle, TensorFlow a
 This section describes how to obtain and prepare your model for work with OpenVINO to get the best inference results:
 
 * :doc:`See the supported formats and how to use them in your project <Supported_Model_Formats>`.
-* :doc:`Convert different model formats to the ov.Model format <openvino_docs_OV_Converter _UG_Deep_Learning_Model_Optimizer_DevGuide>`.
+* :doc:`Convert different model formats to the ov.Model format <openvino_docs_OV_Converter_UG_Deep_Learning_Model_Optimizer_DevGuide>`.
 * :doc:`Transition guide from mo / ov.tools.mo.convert_model to OVC / openvino.convert_model <openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition>`
 
 @endsphinxdirective
diff --git a/docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md b/docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md
@@ -14,6 +14,7 @@
    openvino_docs_MO_DG_FP16_Compression
    openvino_docs_MO_DG_Python_API
    openvino_docs_MO_DG_prepare_model_Model_Optimizer_FAQ
+   Supported_Model_Formats_MO_DG
 
 .. meta::
    :description: Model conversion (MO) furthers the transition between training and

diff --git a/docs/OV_Converter_UG/prepare_model/convert_model/Convert_Model_From_PyTorch.md b/docs/OV_Converter_UG/prepare_model/convert_model/Convert_Model_From_PyTorch.md
@@ -112,7 +112,7 @@ Exporting a PyTorch Model to ONNX Format
 An alternative method of converting PyTorch models is exporting a PyTorch model to ONNX with `torch.onnx.export` first and then converting the resulting `.onnx` file to OpenVINO Model with `openvino.convert_model`. It can be considered as a backup solution if a model cannot be converted directly from PyTorch to OpenVINO as described in the above chapters. Converting through ONNX can be more expensive in terms of code, conversion time, and allocated memory.
 
 1. Refer to the `Exporting PyTorch models to ONNX format <https://pytorch.org/docs/stable/onnx.html>`__ guide to learn how to export models from PyTorch to ONNX.
-2. Follow :doc:`Convert the ONNX model <openvino_docs_OVC_Converter_UG_prepare_model_convert_model_Convert_Model_From_ONNX>` chapter to produce OpenVINO model.
+2. Follow :doc:`Convert the ONNX model <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_ONNX>` chapter to produce OpenVINO model.
 
 Here is an illustration of those two steps used together: