Update quantization-aware-training-pytorch.rst (openvinotoolkit#26414)

The suggested learning rate in this document seems wrong. It should be
1e-5 rather than 10e-5.

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docs/articles_en/openvino-workflow/model-optimization-guide/compressing-models-during-training/quantization-aware-training-pytorch.rst

@@ -18,7 +18,7 @@ Quantize the model using the :doc:`Post-Training Quantization <../quantizing-mod
 2. Fine-tune the Model
 ########################
 
-This step assumes applying fine-tuning to the model the same way it is done for the baseline model. For QAT, it is required to train the model for a few epochs with a small learning rate, for example, 10e-5.
+This step assumes applying fine-tuning to the model the same way it is done for the baseline model. For QAT, it is required to train the model for a few epochs with a small learning rate, for example, 1e-5.
 Quantized models perform all computations in floating-point precision during fine-tuning by modeling quantization errors in both forward and backward passes.
 
 .. doxygensnippet:: docs/optimization_guide/nncf/code/qat_torch.py