Minor

docs/articles_en/openvino-workflow/torch-compile.rst

@@ -329,12 +329,12 @@ Stable Diffusion WebUI is supported on Intel CPUs, Intel integrated GPUs, and In
 
 
 Model Quantization and Weights Compression
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 Model quantization and weights compression are effective methods for accelerating model inference and reducing memory consumption, with minimal impact on model accuracy.
 The ``torch.compile`` OpenVINO backend supports two key model optimization APIs:
 
-1. Neural Network Compression Framework(`NNCF <https://docs.openvino.ai/2024/openvino-workflow/model-optimization.html>`__). NNCF offers advanced algorithms for post-training quantization and weights compression in the OpenVINO toolkit.
+1. Neural Network Compression Framework (`NNCF <https://docs.openvino.ai/2024/openvino-workflow/model-optimization.html>`__). NNCF offers advanced algorithms for post-training quantization and weights compression in the OpenVINO toolkit.
 
 2. PyTorch 2 export quantization. A general-purpose API designed for quantizing models captured by ``torch.export``.
 
@@ -344,7 +344,7 @@ NNCF is the recommended approach for model quantization and weights compression.
 NNCF Model Optimization Support (Preview)
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-The Neural Network Compression Framework(`NNCF <https://docs.openvino.ai/2024/openvino-workflow/model-optimization.html>`__) implements advanced quantization and weights compression algorithms, which can be applied to ``torch.fx.GraphModule`` to speed up inference
+The Neural Network Compression Framework (`NNCF <https://docs.openvino.ai/2024/openvino-workflow/model-optimization.html>`__) implements advanced quantization and weights compression algorithms, which can be applied to ``torch.fx.GraphModule`` to speed up inference
 and decrease memory consumption.
 
 Model quantization example: