fix invalid links

chensuyue · chensuyue · commit aa83c851d2fd · 2025-11-05T14:56:29.000+08:00
Signed-off-by: chensuyue &lt;suyue.chen@intel.com&gt;
diff --git a/.github/checkgroup.yml b/.github/checkgroup.yml
@@ -36,7 +36,7 @@ subprojects:
     paths:
       - "neural_compressor/common/**"
       - "neural_compressor/torch/**"
-      - "examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/llm/**"
+      - "examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm/**"
       - "setup.py"
       - "requirements_pt.txt"
       - ".azure-pipelines/scripts/models/**"
diff --git a/README.md b/README.md
@@ -116,7 +116,7 @@ model = load(
       <td colspan="2" align="center"><a href="./docs/source/3x/design.md#workflows">Workflow</a></td>
       <td colspan="2" align="center"><a href="https://intel.github.io/neural-compressor/latest/docs/source/api-doc/apis.html">APIs</a></td>
       <td colspan="1" align="center"><a href="./docs/source/llm_recipes.md">LLMs Recipes</a></td>
-      <td colspan="1" align="center"><a href="./examples/3.x_api/README.md">Examples</a></td>
+      <td colspan="1" align="center"><a href="./examples/README.md">Examples</a></td>
     </tr>
   </tbody>
   <thead>
diff --git a/docs/source/3x/PT_MixedPrecision.md b/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/pytorch/cv/mixed_precision
 ) on how to quantize a model with Mixed Precision.
diff --git a/docs/source/3x/PT_SmoothQuant.md b/docs/source/3x/PT_SmoothQuant.md
@@ -46,7 +46,7 @@ run_fn(prepared_model)
 q_model = convert(prepared_model)
 ```
 
-To get more information, please refer to [examples](https://github.com/intel/neural-compressor/blob/master/examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/smooth_quant).
+To get more information, please refer to [examples](https://github.com/intel/neural-compressor/blob/master/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/smooth_quant).
 
 
 ## Validated Models
diff --git a/docs/source/3x/PT_StaticQuant.md b/docs/source/3x/PT_StaticQuant.md
@@ -68,7 +68,7 @@ q_model = convert(prepared_model)
 
 #### Model Examples
 
-Users could refer to [examples](https://github.com/intel/neural-compressor/blob/master/examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/static_quant/ipex) on how to quantize a new model.
+Users could refer to [examples](https://github.com/intel/neural-compressor/blob/master/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/static_quant/ipex) on how to quantize a new model.
 
 
 ### Static Quantization with PT2E Backend
@@ -105,4 +105,4 @@ opt_model = torch.compile(q_model)
 
 #### Model Examples with PT2E
 
-Users could refer to [cv examples](https://github.com/intel/neural-compressor/blob/master/examples/3.x_api/pytorch/cv/static_quant) and [llm examples](https://github.com/intel/neural-compressor/blob/master/examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/static_quant/pt2e) on how to quantize a new model.
+Users could refer to [cv examples](https://github.com/intel/neural-compressor/blob/master/examples/pytorch/cv/static_quant) and [llm examples](https://github.com/intel/neural-compressor/blob/master/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/static_quant/pt2e) on how to quantize a new model.
diff --git a/docs/source/3x/PT_WeightOnlyQuant.md b/docs/source/3x/PT_WeightOnlyQuant.md
@@ -339,7 +339,7 @@ For client machines with limited RAM and cores, we offer optimizations to reduce
 
 ## Examples
 
-Users can also refer to [examples](https://github.com/intel/neural-compressor/blob/master/examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/weight_only) on how to quantize a  model with WeightOnlyQuant.
+Users can also refer to [examples](https://github.com/intel/neural-compressor/blob/master/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/weight_only) on how to quantize a  model with WeightOnlyQuant.
 
 ## Reference
 
diff --git a/docs/source/3x/TF_Quant.md b/docs/source/3x/TF_Quant.md
@@ -120,4 +120,4 @@ quant_config.set_local("conv1", conv2d_config)
 
 ## Examples
 
-Users can also refer to [examples](https://github.com/intel/neural-compressor/blob/master/examples/3.x_api/tensorflow) on how to quantize a TensorFlow model with `neural_compressor.tensorflow`.
+Users can also refer to [examples](https://github.com/intel/neural-compressor/blob/master/examples/tensorflow) on how to quantize a TensorFlow model with `neural_compressor.tensorflow`.
diff --git a/docs/source/3x/TF_SQ.md b/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/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant) on how to apply smooth quant to a TensorFlow model with `neural_compressor.tensorflow`.
diff --git a/docs/source/3x/transformers_like_api.md b/docs/source/3x/transformers_like_api.md
@@ -200,7 +200,7 @@ gen_text = tokenizer.batch_decode(gen_ids, skip_special_tokens=True)
 print(gen_text)
 ```
 
-5. You can directly use [example script](https://github.com/intel/neural-compressor/blob/master/examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/transformers/weight_only/text-generation/run_generation_gpu_woq.py)
+5. You can directly use [example script](https://github.com/intel/neural-compressor/blob/master/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/transformers/weight_only/text-generation/run_generation_gpu_woq.py)
 ```bash
 python run_generation_gpu_woq.py --woq --benchmark --model save_dir
 ```
@@ -213,4 +213,4 @@ python run_generation_gpu_woq.py --woq --benchmark --model save_dir
 
 ## Examples
 
-Users can also refer to [examples](https://github.com/intel/neural-compressor/blob/master/examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/transformers/weight_only/text-generation) on how to quantize a model with transformers-like api.
+Users can also refer to [examples](https://github.com/intel/neural-compressor/blob/master/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/transformers/weight_only/text-generation) on how to quantize a model with transformers-like api.
diff --git a/examples/pytorch/cv/mixed_precision/README.md b/examples/pytorch/cv/mixed_precision/README.md
@@ -10,7 +10,7 @@ This document describes the step-by-step instructions for reproducing PyTorch Re
 PyTorch 1.8 or higher version is needed with pytorch_fx backend.
 
 ```Shell
-cd examples/3.x_api/pytorch/image_recognition/torchvision_models/mixed_precision/resnet18
+cd examples/pytorch/image_recognition/torchvision_models/mixed_precision/resnet18
 pip install -r requirements.txt
 ```
 > Note: Validated PyTorch [Version](/docs/source/installation_guide.md#validated-software-environment).
diff --git a/examples/pytorch/multimodal-modeling/quantization/auto_round/llama4/README.md b/examples/pytorch/multimodal-modeling/quantization/auto_round/llama4/README.md
@@ -10,7 +10,7 @@ This example quantizes and validates the accuracy of Llama4.
 docker run -d --gpus all -v ... --shm-size=100g --name llama4 -it nvcr.io/nvidia/pytorch:25.05-py3 /bin/bash
 docker exec -it llama4 bash
 git clone https://github.com/intel/neural-compressor.git
-cd neural-compressor/examples/3.x_api/pytorch/multimodal-modeling/quantization/auto_round/llama4
+cd neural-compressor/examples/pytorch/multimodal-modeling/quantization/auto_round/llama4
 # Use `INC_PT_ONLY=1 pip install git+https://github.com/intel/neural-compressor.git@v3.6rc` for the latest updates before neural-compressor v3.6 release
 pip install neural-compressor-pt==3.6
 # Use `pip install git+https://github.com/intel/auto-round.git@v0.8.0rc2` for the latest updates before auto-round v0.8.0 release
diff --git a/examples/tensorflow/image_recognition/inception_v3/quantization/ptq/README.md b/examples/tensorflow/image_recognition/inception_v3/quantization/ptq/README.md
@@ -44,10 +44,10 @@ pip install --upgrade intel-extension-for-tensorflow[cpu]
 ## 3. Prepare Dataset
 
   TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
-  We also prepared related scripts in ` examples/3.x_api/tensorflow/cv` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
+  We also prepared related scripts in ` examples/tensorflow/cv` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
 
   ```shell
-  cd examples/3.x_api/tensorflow/cv
+  cd examples/tensorflow/cv
   # convert validation subset
   bash prepare_dataset.sh --output_dir=./inception_v3/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
   # convert train subset
diff --git a/examples/tensorflow/image_recognition/mobilenet_v2/quantization/ptq/README.md b/examples/tensorflow/image_recognition/mobilenet_v2/quantization/ptq/README.md
@@ -77,10 +77,10 @@ We can get the pb file by convert the checkpoint file.
 ## 3. Prepare Dataset
 
   TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
-  We also prepared related scripts in ` examples/3.x_api/tensorflow/cv` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
+  We also prepared related scripts in ` examples/tensorflow/cv` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
 
   ```shell
-  cd examples/3.x_api/tensorflow/cv
+  cd examples/tensorflow/cv
   # convert validation subset
   bash prepare_dataset.sh --output_dir=./mobilenet_v2/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
   # convert train subset
diff --git a/examples/tensorflow/image_recognition/resnet_v2_50/quantization/ptq/README.md b/examples/tensorflow/image_recognition/resnet_v2_50/quantization/ptq/README.md
@@ -76,10 +76,10 @@ We can get the pb file by convert the checkpoint file.
 ## 3. Prepare Dataset
 
   TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
-  We also prepared related scripts in ` examples/3.x_api/tensorflow/cv` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
+  We also prepared related scripts in ` examples/tensorflow/cv` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
 
   ```shell
-  cd examples/3.x_api/tensorflow/cv
+  cd examples/tensorflow/cv
   # convert validation subset
   bash prepare_dataset.sh --output_dir=./resnet_v2_50/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
   # convert train subset
diff --git a/examples/tensorflow/image_recognition/vgg16/quantization/ptq/README.md b/examples/tensorflow/image_recognition/vgg16/quantization/ptq/README.md
@@ -77,10 +77,10 @@ We can get the pb file by convert the checkpoint file.
 ## 3. Prepare Dataset
 
   TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
-  We also prepared related scripts in `examples/3.x_api/tensorflow/cv` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
+  We also prepared related scripts in `examples/tensorflow/cv` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
 
   ```shell
-  cd examples/3.x_api/tensorflow/cv
+  cd examples/tensorflow/cv
   # convert validation subset
   bash prepare_dataset.sh --output_dir=./vgg16/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
   # convert train subset
diff --git a/examples/tensorflow/image_recognition/vision_transformer/quantization/ptq/README.md b/examples/tensorflow/image_recognition/vision_transformer/quantization/ptq/README.md
@@ -40,10 +40,10 @@ wget https://storage.googleapis.com/intel-optimized-tensorflow/models/2_11_0/HF-
 ## 3. Prepare Dataset
 
   TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
-  We also prepared related scripts in `examples/3.x_api/tensorflow/cv` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
+  We also prepared related scripts in `examples/tensorflow/cv` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
 
   ```shell
-  cd examples/3.x_api/tensorflow/cv
+  cd examples/tensorflow/cv
   # convert validation subset
   bash prepare_dataset.sh --output_dir=./vision_transformer/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
   # convert train subset
diff --git a/examples/tensorflow/keras/image_recognition/inception_v3/quantization/ptq/README.md b/examples/tensorflow/keras/image_recognition/inception_v3/quantization/ptq/README.md
@@ -38,7 +38,7 @@ python prepare_model.py --output_model=./inception_v3_keras
   We also prepared related scripts in `imagenet_prepare` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
 
   ```shell
-  cd examples/3.x_api/tensorflow/keras/cv/
+  cd examples/tensorflow/keras/cv/
   # convert validation subset
   bash prepare_dataset.sh --output_dir=./inception_v3/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
   # convert train subset
diff --git a/examples/tensorflow/keras/image_recognition/resnet_v2_50/quantization/ptq/README.md b/examples/tensorflow/keras/image_recognition/resnet_v2_50/quantization/ptq/README.md
@@ -37,7 +37,7 @@ python prepare_model.py --output_model=./resnetv2_50_keras
   We also prepared related scripts in `imagenet_prepare` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
 
   ```shell
-  cd examples/3.x_api/tensorflow/keras/cv/
+  cd examples/tensorflow/keras/cv/
   # convert validation subset
   bash prepare_dataset.sh --output_dir=./resnetv2_50/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
   # convert train subset
diff --git a/examples/tensorflow/nlp/transformer_lt/quantization/ptq/README.md b/examples/tensorflow/nlp/transformer_lt/quantization/ptq/README.md
@@ -50,10 +50,10 @@ tar -zxvf transformer_lt_official_fp32_pretrained_model.tar.gz
 Dataset is in data folder, pretrained model is in graph folder.
 
 #### Automatic dataset & model download
-Run the `prepare_dataset_model.sh` script located in `examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq`.
+Run the `prepare_dataset_model.sh` script located in `examples/tensorflow/nlp/transformer_lt/quantization/ptq`.
 
 ```shell
-cd examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq
+cd examples/tensorflow/nlp/transformer_lt/quantization/ptq
 bash prepare_dataset_model.sh
 ```
 
diff --git a/examples/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/README.md b/examples/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/README.md
@@ -22,7 +22,7 @@ pip install tensorflow
 
 ### Installation Dependency packages
 ```shell
-cd examples/3.x_api/tensorflow/object_detection
+cd examples/tensorflow/object_detection
 pip install -r requirements.txt
 cd faster_rcnn_resnet50/quantization/ptq
 ```
@@ -65,11 +65,11 @@ tar -xvf faster_rcnn_resnet50_fp32_coco_pretrained_model.tar.gz
 
 > **_Note: `prepare_dataset.sh` script works with TF version 1.x._**
 
-Run the `prepare_dataset.sh` script located in `examples/3.x_api/tensorflow/object_detection`.
+Run the `prepare_dataset.sh` script located in `examples/tensorflow/object_detection`.
 
 Usage:
 ```shell
-cd examples/3.x_api/tensorflow/object_detection
+cd examples/tensorflow/object_detection
 . prepare_dataset.sh
 cd faster_rcnn_resnet50/quantization/ptq
 ```
diff --git a/examples/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/README.md b/examples/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/README.md
@@ -22,7 +22,7 @@ pip install intel-tensorflow
 
 ### Installation Dependency packages
 ```shell
-cd examples/3.x_api/tensorflow/object_detection
+cd examples/tensorflow/object_detection
 pip install -r requirements.txt
 cd mask_rcnn_inception_v2/quantization/ptq
 ```
@@ -65,11 +65,11 @@ tar -xvzf mask_rcnn_inception_v2_coco_2018_01_28.tar.gz
 
 > **_Note: `prepare_dataset.sh` script works with TF version 1.x._**
 
-Run the `prepare_dataset.sh` script located in `examples/3.x_api/tensorflow/object_detection`.
+Run the `prepare_dataset.sh` script located in `examples/tensorflow/object_detection`.
 
 Usage:
 ```shell
-cd examples/3.x_api/tensorflow/object_detection/
+cd examples/tensorflow/object_detection/
 . prepare_dataset.sh
 cd mask_rcnn_inception_v2/quantization/ptq
 ```
diff --git a/examples/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/README.md b/examples/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/README.md
@@ -22,7 +22,7 @@ pip install intel-tensorflow
 
 ### Installation Dependency packages
 ```shell
-cd examples/3.x_api//tensorflow/object_detection
+cd examples/tensorflow/object_detection
 pip install -r requirements.txt
 cd ssd_mobilenet_v1/quantization/ptq
 ```
@@ -55,7 +55,7 @@ pip install --upgrade intel-extension-for-tensorflow[cpu]
 ## 2. Prepare Model
 
 ### Automated approach
-Run the `prepare_model.py` script located in `examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq`.
+Run the `prepare_model.py` script located in `examples/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq`.
 
 ```
 python prepare_model.py --model_name=ssd_mobilenet_v1 --model_path=./
@@ -83,11 +83,11 @@ tar -xvzf ssd_mobilenet_v1_coco_2018_01_28.tar.gz
 
 > **_Note: `prepare_dataset.sh` script works with TF version 1.x._**
 
-Run the `prepare_dataset.sh` script located in `examples/3.x_api/tensorflow/object_detection`.
+Run the `prepare_dataset.sh` script located in `examples/tensorflow/object_detection`.
 
 Usage:
 ```shell
-cd examples/3.x_api/tensorflow/object_detection
+cd examples/tensorflow/object_detection
 . prepare_dataset.sh
 cd ssd_mobilenet_v1/quantization/ptq
 ```
diff --git a/examples/tensorflow/object_detection/yolo_v5/quantization/ptq/README.md b/examples/tensorflow/object_detection/yolo_v5/quantization/ptq/README.md
@@ -19,7 +19,7 @@ pip install tensorflow
 
 ### Installation Dependency packages
 ```shell
-cd examples/3.x_api/tensorflow/object_detection/yolo_v5/quantization/ptq
+cd examples/tensorflow/object_detection/yolo_v5/quantization/ptq
 pip install -r requirements.txt
 ```
 
diff --git a/examples/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/README.md b/examples/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/README.md
@@ -41,7 +41,7 @@ pip install --upgrade intel-extension-for-tensorflow[cpu]
 
 ### Install Additional Dependency packages
 ```shell
-cd examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq
+cd examples/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq
 pip install -r requirements.txt
 ```
 

Original file line number	Diff line number	Diff line change
`@@ -120,4 +120,4 @@ quant_config.set_local("conv1", conv2d_config)`
`120`	`120`
`121`	`121`	`## Examples`
`122`	`122`
`123`		-Users can also refer to [examples](https://github.com/intel/neural-compressor/blob/master/examples/3.x_api/tensorflow) on how to quantize a TensorFlow model with `neural_compressor.tensorflow`.
	`123`	+Users can also refer to [examples](https://github.com/intel/neural-compressor/blob/master/examples/tensorflow) on how to quantize a TensorFlow model with `neural_compressor.tensorflow`.
Original file line number	Diff line number	Diff line change
`@@ -50,4 +50,4 @@ best_model = autotune(`
`50`	`50`
`51`	`51`	`## Examples`
`52`	`52`
`53`		-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`.
	`53`	+Users can also refer to [examples](https://github.com/intel/neural-compressor/blob/master/examples/tensorflow/nlp/large_language_models/quantization/ptq/smoothquant) on how to apply smooth quant to a TensorFlow model with `neural_compressor.tensorflow`.