Fix broken link in docs

docs/source/3x/PT_MixedPrecision.md

@@ -107,5 +107,5 @@ best_model = autotune(model=build_torch_model(), tune_config=custom_tune_config,
 
 ## Examples
 
-Users can also refer to [examples](https://github.com/intel/neural-compressor/blob/master/examples/3.x_api/pytorch\cv\mixed_precision
+Users can also refer to [examples](https://github.com/intel/neural-compressor/blob/master/examples/3.x_api/pytorch/cv/mixed_precision
 ) on how to quantize a model with Mixed Precision.

docs/source/3x/TF_Quant.md

@@ -13,7 +13,7 @@ TensorFlow Quantization
 
 `neural_compressor.tensorflow` supports quantizing both TensorFlow and Keras model with or without accuracy aware tuning.
 
-For the detailed quantization fundamentals, please refer to the document for [Quantization](../quantization.md).
+For the detailed quantization fundamentals, please refer to the document for [Quantization](quantization.md).
 
 
 ## Get Started

docs/source/3x/TF_SQ.md

@@ -50,4 +50,4 @@ best_model = autotune(
 
 ## Examples
 
-Users can also refer to [examples](https://github.com/intel/neural-compressor/blob/master/examples/3.x_api/tensorflow/nlp/large_language_models\quantization\ptq\smoothquant) on how to apply smooth quant to a TensorFlow model with `neural_compressor.tensorflow`.
+Users can also refer to [examples](https://github.com/intel/neural-compressor/blob/master/examples/3.x_api/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant) on how to apply smooth quant to a TensorFlow model with `neural_compressor.tensorflow`.

docs/source/3x/quantization.md

@@ -396,7 +396,7 @@ For supported quantization methods for `accuracy aware tuning` and the detailed
 
 User could refer to below chart to understand the whole tuning flow.
 
-<img src="../source/imgs/accuracy_aware_tuning_flow.png" width=600 height=480 alt="accuracy aware tuning working flow">
+<img src="./imgs/workflow.png" alt="accuracy aware tuning working flow">