[DOCS] Delete DL WB note (#15225)

* delete DL WB tips

* remove space

* fixed a link

docs/Documentation/model_introduction.md

@@ -22,7 +22,7 @@ The approach to fully convert a model is considered the default choice, as it al
 
 Conversion is not required for ONNX and PaddlePaddle models, as OpenVINO provides C++ and Python APIs for importing them to OpenVINO Runtime directly. It provides a convenient way to quickly switch from framework-based code to OpenVINO-based code in your inference application.
 
-This section describes the how to obtain and prepare your model for work with OpenVINO to get the best inference results:
+This section describes how to obtain and prepare your model for work with OpenVINO to get the best inference results:
 * [See the supported formats and how to use them in your project](../MO_DG/prepare_model/convert_model/supported_model_formats.md)
 * [Convert different model formats to the OpenVINO IR format](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 * [Automate model-related tasks with Model Downloader and additional OMZ Tools](https://docs.openvino.ai/latest/omz_tools_downloader.html).

docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md

@@ -33,10 +33,7 @@ where IR is a pair of files describing the model:
 
 *  <code>.bin</code> - Contains the weights and biases binary data.
 
-The OpenVINO IR can be additionally optimized for inference by [Post-training optimization](../../tools/pot/docs/Introduction.md)
-> that applies post-training quantization methods.
-
-> **TIP**: You can also work with Model Optimizer in OpenVINO™ [Deep Learning Workbench (DL Workbench)](https://docs.openvino.ai/latest/workbench_docs_Workbench_DG_Introduction.html), which is a web-based tool with GUI for optimizing, fine-tuning, analyzing, visualizing, and comparing performance of deep learning models.
+The OpenVINO IR can be additionally optimized for inference by [Post-training optimization](../../tools/pot/docs/Introduction.md) that applies post-training quantization methods.
 
 ## How to Run Model Optimizer
 
@@ -86,7 +83,7 @@ For more information, refer to the [Converting a TensorFlow Model](prepare_model
 ```sh
 mo --input_model ocr.onnx --output probabilities
 ```
-For more information, refer to the [Converting an ONNX Model (prepare_model/convert_model/Convert_Model_From_ONNX.md) guide.
+For more information, refer to the [Converting an ONNX Model](prepare_model/convert_model/Convert_Model_From_ONNX.md) guide.
 
 > **NOTE**: PyTorch models must be exported to the ONNX format before conversion into IR. More information can be found in [Converting a PyTorch Model](prepare_model/convert_model/Convert_Model_From_PyTorch.md).