Fix links in readme

README.md

@@ -76,8 +76,8 @@ You can customize data generation configurations to suit your specific needs. [G
 
 ### Quantization
 MCT supports different quantization methods:
-* Post-training quantization (PTQ): [Keras API](https://sony.github.io/model_optimization/docs/api/experimental_api_docs/methods/keras_post_training_quantization_experimental.html#ug-keras-post-training-quantization-experimental), [PyTorch API](https://sony.github.io/model_optimization/docs/api/experimental_api_docs/methods/pytorch_post_training_quantization_experimental.html#ug-pytorch-post-training-quantization-experimental)
-* Gradient-based post-training quantization (GPTQ): [Keras API](https://sony.github.io/model_optimization/docs/api/experimental_api_docs/methods/keras_gradient_post_training_quantization_experimental.html#ug-keras-gradient-post-training-quantization-experimental), [PyTorch API](https://sony.github.io/model_optimization/docs/api/experimental_api_docs/methods/pytorch_gradient_post_training_quantization_experimental.html#ug-pytorch-gradient-post-training-quantization-experimental)
+* Post-training quantization (PTQ): [Keras API](https://sony.github.io/model_optimization/docs/api/api_docs/methods/keras_post_training_quantization.html), [PyTorch API](https://sony.github.io/model_optimization/docs/api/api_docs/methods/pytorch_post_training_quantization.html)
+* Gradient-based post-training quantization (GPTQ): [Keras API](https://sony.github.io/model_optimization/docs/api/api_docs/methods/keras_gradient_post_training_quantization.html), [PyTorch API](https://sony.github.io/model_optimization/docs/api/api_docs/methods/pytorch_gradient_post_training_quantization.html)
 * Quantization-aware training (QAT) [*](#experimental-features)
 
 
@@ -120,8 +120,8 @@ taking into account the target platform's Single Instruction, Multiple Data (SIM
 By pruning groups of channels (SIMD groups), our approach not only reduces model size 
 and complexity, but ensures that better utilization of channels is in line with the SIMD architecture 
 for a target Resource Utilization of weights memory footprint.
-[Keras API](https://sony.github.io/model_optimization/docs/api/experimental_api_docs/methods/keras_pruning_experimental.html)
-[Pytorch API](https://github.com/sony/model_optimization/blob/main/model_compression_toolkit/pruning/pytorch/pruning_facade.py#L43) 
+[Keras API](https://sony.github.io/model_optimization/docs/api/api_docs/methods/keras_pruning_experimental.html)
+[Pytorch API](https://sony.github.io/model_optimization/docs/api/api_docs/methods/pytorch_pruning_experimental.html) 
 
 #### Experimental features 
 

model_compression_toolkit/target_platform_capabilities/README.md

@@ -13,7 +13,7 @@ in some operator for its weights/activations, fusing patterns, etc.)
 ## Supported Target Platform Models 
 
 Currently, MCT contains three target-platform models
-(new models can be created and used by users as demonstrated [here](https://sony.github.io/model_optimization/docs/api/experimental_api_docs/modules/target_platform.html#targetplatformmodel-code-example)):
+(new models can be created and used by users as demonstrated [here](https://sony.github.io/model_optimization/docs/api/api_docs/modules/target_platform.html#targetplatformmodel-code-example)):
 - [IMX500](https://developer.sony.com/develop/imx500/)
 - [TFLite](https://www.tensorflow.org/lite/performance/quantization_spec)
 - [QNNPACK](https://github.com/pytorch/QNNPACK)
@@ -27,7 +27,7 @@ One may view the full default target-platform model and its parameters [here](ht
 
 ## Usage
 
-The simplest way to initiate a TPC and use it in MCT is by using the function [get_target_platform_capabilities](https://sony.github.io/model_optimization/docs/api/experimental_api_docs/methods/get_target_platform_capabilities.html#ug-get-target-platform-capabilities).
+The simplest way to initiate a TPC and use it in MCT is by using the function [get_target_platform_capabilities](https://sony.github.io/model_optimization/docs/api/api_docs/methods/get_target_platform_capabilities.html#ug-get-target-platform-capabilities).
 
 For example:
 
@@ -50,4 +50,4 @@ quantized_model, quantization_info = mct.ptq.keras_post_training_quantization(Mo
 
 Similarly, you can retrieve IMX500, TFLite and QNNPACK target-platform models for Keras and PyTorch frameworks.
 
-For more information and examples, we highly recommend you to visit our [project website](https://sony.github.io/model_optimization/docs/api/experimental_api_docs/modules/target_platform.html#ug-target-platform).
+For more information and examples, we highly recommend you to visit our [project website](https://sony.github.io/model_optimization/docs/api/api_docs/modules/target_platform.html#ug-target-platform).