diff --git a/README.md b/README.md
index bcc9c2fcc96..d7f02d5aa02 100644
--- a/README.md
+++ b/README.md
@@ -146,7 +146,7 @@ quantized_model = fit(model=float_model, conf=static_quant_conf, calib_dataloade
       <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/3x/llm_recipes.md">LLMs Recipes</a></td>
-      <td colspan="1" align="center">Examples</td>
+      <td colspan="1" align="center"><a href="./examples/3.x_api/README.md">Examples</a></td>
     </tr>
   </tbody>
   <thead>
diff --git a/docs/source/llm_recipes.md b/docs/source/llm_recipes.md
index 328bba3ba09..8a9c17e7cd7 100644
--- a/docs/source/llm_recipes.md
+++ b/docs/source/llm_recipes.md
@@ -17,8 +17,8 @@ This document aims to publish the specific recipes we achieved for the popular L
 |       EleutherAI/gpt-j-6b       |    ✔    |    ✔     |    ✔     |
 |        facebook/opt-1.3b        |    ✔    |    ✔     |    ✔     |
 |        facebook/opt-30b         |    ✔    |    ✔     |    ✔     |
-|    meta-llama/Llama-2-7b-hf     |    ✔    |    ✔     |    ✔     |
-|    meta-llama/Llama-2-13b-hf    |    ✔    |    ✔     |    ✔     |
+|    meta-llama/Llama-2-7b-hf     |   WIP   |    ✔     |    ✔     |
+|    meta-llama/Llama-2-13b-hf    |   WIP   |    ✔     |    ✔     |
 |    meta-llama/Llama-2-70b-hf    |    ✔    |    ✔     |    ✔     |
 |        tiiuae/falcon-7b         |    ✔    |    ✔     |    ✔     |
 |        tiiuae/falcon-40b        |    ✔    |    ✔     |    ✔     |
@@ -29,8 +29,8 @@ This document aims to publish the specific recipes we achieved for the popular L
 |     databricks/dolly-v2-12b     |    ✖    |    ✔     |    ✖     |
 |     EleutherAI/gpt-neox-20b     |    ✖    |    ✔     |    ✔     |
 |    mistralai/Mistral-7B-v0.1    |    ✖    |    ✔     |    ✔     |
-|        THUDM/chatglm2-6b        |    ✔    |    ✔     |    ✔     |
-|        THUDM/chatglm3-6b        |   WIP   |    ✔     |   WIP    |
+|        THUDM/chatglm2-6b        |   WIP   |    ✔     |   WIP    |
+|        THUDM/chatglm3-6b        |   WIP   |    ✔     |    ✔     |
 
 **Detail recipes can be found [HERE](https://github.com/intel/intel-extension-for-transformers/blob/main/examples/huggingface/pytorch/text-generation/quantization/llm_quantization_recipes.md).**
 
@@ -68,48 +68,48 @@ This document aims to publish the specific recipes we achieved for the popular L
   <tr>
     <td>baichuan-inc/Baichuan-13B-Chat</td>
     <td>67.57%</td>
-    <td>69.07%</td>
-    <td>1.0222</td>
+    <td>67.86%</td>
+    <td>1.0043</td>
     <td>67.55%</td>
     <td>0.9997</td>
-    <td>68.12%</td>
-    <td>1.0081</td>
-    <td>66.93%</td>
-    <td>0.9905</td>
+    <td>67.46%</td>
+    <td>0.9984</td>
+    <td>N/A</td>
+    <td>N/A</td>
   </tr>
   <tr>
     <td>baichuan-inc/Baichuan2-13B-Chat</td>
     <td>71.51%</td>
-    <td>75.57%</td>
-    <td>1.0568</td>
+    <td>75.51%</td>
+    <td>1.0559</td>
     <td>71.57%</td>
     <td>1.0008</td>
-    <td>70.81%</td>
-    <td>0.9902</td>
-    <td>N/A</td>
-    <td>N/A</td>
+    <td>71.45%</td>
+    <td>0.9992</td>
+    <td>70.87%</td>
+    <td>0.9911</td>
   </tr>
   <tr>
     <td>baichuan-inc/Baichuan2-7B-Chat</td>
     <td>67.67%</td>
-    <td>68.06%</td>
-    <td>1.0058</td>
+    <td>67.51%</td>
+    <td>0.9976</td>
     <td>67.61%</td>
     <td>0.9991</td>
-    <td>67.90%</td>
-    <td>1.0034</td>
-    <td>N/A</td>
-    <td>N/A</td>
+    <td>68.08%</td>
+    <td>1.0061</td>
+    <td>67.18%</td>
+    <td>0.9928</td>
   </tr>
   <tr>
     <td>bigscience/bloom-1b7</td>
     <td>46.34%</td>
-    <td>47.99%</td>
-    <td>1.0356</td>
+    <td>47.97%</td>
+    <td>1.0352</td>
     <td>46.21%</td>
     <td>0.9972</td>
-    <td>46.90%</td>
-    <td>1.0121</td>
+    <td>47.00%</td>
+    <td>1.0142</td>
     <td>N/A</td>
     <td>N/A</td>
   </tr>
@@ -128,14 +128,14 @@ This document aims to publish the specific recipes we achieved for the popular L
   <tr>
     <td>EleutherAI/gpt-j-6b</td>
     <td>68.31%</td>
+    <td>68.00%</td>
+    <td>0.9955</td>
     <td>68.27%</td>
     <td>0.9994</td>
-    <td>68.27%</td>
-    <td>0.9994</td>
-    <td>68.35%</td>
-    <td>1.0006</td>
-    <td>68.02%</td>
-    <td>0.9958</td>
+    <td>68.23%</td>
+    <td>0.9988</td>
+    <td>67.40%</td>
+    <td>0.9867</td>
   </tr>
   <tr>
     <td>EleutherAI/gpt-neox-20b</td>
@@ -144,68 +144,68 @@ This document aims to publish the specific recipes we achieved for the popular L
     <td>N/A</td>
     <td>72.29%</td>
     <td>0.9994</td>
-    <td>71.74%</td>
-    <td>0.9918</td>
+    <td>72.15%</td>
+    <td>0.9975</td>
     <td>N/A</td>
     <td>N/A</td>
   </tr>
   <tr>
     <td>facebook/opt-1.3b</td>
     <td>57.89%</td>
-    <td>57.68%</td>
-    <td>0.9964</td>
+    <td>57.35%</td>
+    <td>0.9907</td>
     <td>58.12%</td>
     <td>1.0040</td>
-    <td>58.26%</td>
-    <td>1.0064</td>
+    <td>58.01%</td>
+    <td>1.0021</td>
     <td>N/A</td>
     <td>N/A</td>
   </tr>
   <tr>
     <td>facebook/opt-30b</td>
     <td>71.49%</td>
-    <td>71.78%</td>
-    <td>1.0041</td>
+    <td>71.51%</td>
+    <td>1.0003</td>
     <td>71.53%</td>
     <td>1.0006</td>
-    <td>71.59%</td>
-    <td>1.0014</td>
-    <td>71.80%</td>
-    <td>1.0043</td>
+    <td>71.82%</td>
+    <td>1.0046</td>
+    <td>71.43%</td>
+    <td>0.9992</td>
   </tr>
   <tr>
     <td>meta-llama/Llama-2-13b-hf</td>
     <td>76.77%</td>
-    <td>76.25%</td>
-    <td>0.9932</td>
+    <td>N/A</td>
+    <td>N/A</td>
     <td>76.89%</td>
     <td>1.0016</td>
-    <td>77.66%</td>
-    <td>1.0116</td>
-    <td>76.60%</td>
-    <td>0.9978</td>
+    <td>76.96%</td>
+    <td>1.0025</td>
+    <td>N/A</td>
+    <td>N/A</td>
   </tr>
   <tr>
     <td>meta-llama/Llama-2-70b-hf</td>
     <td>79.64%</td>
-    <td>79.14%</td>
-    <td>0.9937</td>
+    <td>79.53%</td>
+    <td>0.9986</td>
     <td>79.62%</td>
     <td>0.9997</td>
-    <td>80.09%</td>
-    <td>1.0057</td>
-    <td>79.68%</td>
-    <td>1.0005</td>
+    <td>80.05%</td>
+    <td>1.0051</td>
+    <td>N/A</td>
+    <td>N/A</td>
   </tr>
   <tr>
     <td>meta-llama/Llama-2-7b-hf</td>
     <td>73.92%</td>
-    <td>73.45%</td>
-    <td>0.9936</td>
+    <td>N/A</td>
+    <td>N/A</td>
     <td>73.90%</td>
     <td>0.9997</td>
-    <td>73.84%</td>
-    <td>0.9989</td>
+    <td>73.51%</td>
+    <td>0.9945</td>
     <td>N/A</td>
     <td>N/A</td>
   </tr>
@@ -216,22 +216,22 @@ This document aims to publish the specific recipes we achieved for the popular L
     <td>N/A</td>
     <td>75.80%</td>
     <td>0.9987</td>
-    <td>76.25%</td>
-    <td>1.0046</td>
-    <td>75.74%</td>
-    <td>0.9979</td>
+    <td>75.37%</td>
+    <td>0.9930</td>
+    <td>75.82%</td>
+    <td>0.9989</td>
   </tr>
   <tr>
     <td>THUDM/chatglm2-6b</td>
     <td>53.23%</td>
-    <td>52.86%</td>
-    <td>0.9930</td>
+    <td>N/A</td>
+    <td>N/A</td>
     <td>53.00%</td>
     <td>0.9957</td>
-    <td>52.90%</td>
-    <td>0.9938</td>
-    <td>52.92%</td>
-    <td>0.9942</td>
+    <td>N/A</td>
+    <td>N/A</td>
+    <td>N/A</td>
+    <td>N/A</td>
   </tr>
   <tr>
     <td>THUDM/chatglm3-6b</td>
@@ -242,31 +242,31 @@ This document aims to publish the specific recipes we achieved for the popular L
     <td>0.9990</td>
     <td>N/A</td>
     <td>N/A</td>
-    <td>N/A</td>
-    <td>N/A</td>
+    <td>58.59%</td>
+    <td>0.9915</td>
   </tr>
   <tr>
     <td>tiiuae/falcon-40b</td>
     <td>77.22%</td>
-    <td>76.95%</td>
-    <td>0.9965</td>
+    <td>77.26%</td>
+    <td>1.0005</td>
     <td>77.18%</td>
     <td>0.9995</td>
-    <td>77.55%</td>
-    <td>1.0043</td>
-    <td>77.82%</td>
-    <td>1.0078</td>
+    <td>77.97%</td>
+    <td>1.0097</td>
+    <td>N/A</td>
+    <td>N/A</td>
   </tr>
   <tr>
     <td>tiiuae/falcon-7b</td>
     <td>74.67%</td>
-    <td>76.63%</td>
-    <td>1.0262</td>
+    <td>76.17%</td>
+    <td>1.0201</td>
     <td>74.73%</td>
     <td>1.0008</td>
-    <td>75.06%</td>
-    <td>1.0052</td>
-    <td>74.00%</td>
-    <td>0.9910</td>
+    <td>74.79%</td>
+    <td>1.0016</td>
+    <td>N/A</td>
+    <td>N/A</td>
   </tr>
 </tbody></table>
diff --git a/examples/.config/model_params_tensorflow_3x.json b/examples/.config/model_params_tensorflow_3x.json
index 74b40ea4f5d..e2a052656f8 100644
--- a/examples/.config/model_params_tensorflow_3x.json
+++ b/examples/.config/model_params_tensorflow_3x.json
@@ -8,20 +8,6 @@
       "batch_size": 64,
       "fp32_model_url": "https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_7_0/fp32_bert_squad.pb"
     },
-    "distilbert_base": {
-      "model_src_dir": "nlp/distilbert_base/quantization/ptq",
-      "dataset_location": "/tf_dataset2/datasets/sst2_validation_dataset",
-      "input_model": "/tf_dataset2/models/tensorflow/distilbert_base/fp32/distilbert_base_fp32.pb",
-      "main_script": "main.py",
-      "batch_size": 128
-    },
-    "distilbert_base_sq": {
-      "model_src_dir": "nlp/distilbert_base/quantization/ptq",
-      "dataset_location": "/tf_dataset2/datasets/sst2_validation_dataset",
-      "input_model": "/tf_dataset2/models/tensorflow/distilbert_base/fp32/distilbert_base_fp32.pb",
-      "main_script": "main.py",
-      "batch_size": 128
-    },
     "opt_125m_sq": {
       "model_src_dir": "nlp/large_language_models/quantization/ptq/smoothquant",
       "dataset_location": "",
@@ -97,9 +83,9 @@
       "model_src_dir": "object_detection/yolo_v5/quantization/ptq",
       "dataset_location": "/tf_dataset2/datasets/coco_yolov5/coco",
       "input_model": "/tf_dataset2/models/tensorflow/yolo_v5/yolov5s.pb",
-      "main_script": "main.py", 
+      "main_script": "main.py",
       "batch_size": 1
-    },     
+    },
     "faster_rcnn_resnet50": {
       "model_src_dir": "object_detection/faster_rcnn_resnet50/quantization/ptq",
       "dataset_location": "/tf_dataset/tensorflow/coco_val.record",
@@ -125,14 +111,14 @@
       "model_src_dir": "object_detection/ssd_mobilenet_v1/quantization/ptq",
       "dataset_location": "/tf_dataset/tensorflow/coco_val.record",
       "input_model": "/tf_dataset/pre-train-model-oob/object_detection/ssd_mobilenet_v1/frozen_inference_graph.pb",
-      "main_script": "main.py",       
+      "main_script": "main.py",
       "batch_size": 10
     },
     "ssd_mobilenet_v1_ckpt": {
       "model_src_dir": "object_detection/ssd_mobilenet_v1/quantization/ptq",
       "dataset_location": "/tf_dataset/tensorflow/coco_val.record",
       "input_model": "/tf_dataset/pre-train-model-oob/object_detection/ssd_mobilenet_v1",
-      "main_script": "main.py",       
+      "main_script": "main.py",
       "batch_size": 10
     },
     "wide_deep_large_ds": {
@@ -158,5 +144,4 @@
       "batch_size": 1
     }
   }
-}
-
+}
\ No newline at end of file
diff --git a/examples/3.x_api/README.md b/examples/3.x_api/README.md
new file mode 100644
index 00000000000..fd79f210533
--- /dev/null
+++ b/examples/3.x_api/README.md
@@ -0,0 +1,169 @@
+# Examples
+
+Intel® Neural Compressor validated examples with multiple compression techniques, including quantization, pruning, knowledge distillation and orchestration. Part of the validated cases can be found in the example tables, and the release data is available [here](../docs/source/validated_model_list.md).
+
+
+# PyTorch Examples
+
+## Quantization
+<table>
+<thead>
+  <tr>
+    <th>Model</th>
+    <th>Domain</th>
+    <th>Method </th>
+    <th>Examples</th>
+  </tr>
+</thead>
+<tbody>
+
+<tr>
+    <td rowspan="2">gpt_j</td>
+    <td rowspan="2">Natural Language Processing</td>
+    <td>Weight-Only Quantization</td>
+    <td><a href="./pytorch/nlp/huggingface_models/language-modeling/quantization/weight_only">link</a></td>
+</tr>
+<tr>
+    <td>Static Quantization (IPEX)</td>
+    <td><a href="./pytorch/nlp/huggingface_models/language-modeling/quantization/static_quant/ipex">link</a></td>
+</tr>
+<tr>
+    <td rowspan="2">llama2_7b</td>
+    <td rowspan="2">Natural Language Processing</td>
+    <td>Weight-Only Quantization</td>
+    <td><a href="./pytorch/nlp/huggingface_models/language-modeling/quantization/weight_only">link</a></td>
+</tr>
+<tr>
+    <td>Static Quantization (IPEX)</td>
+    <td><a href="./pytorch/nlp/huggingface_models/language-modeling/quantization/static_quant/ipex">link</a></td>
+</tr>
+<tr>
+    <td rowspan="3">opt_125m</td>
+    <td rowspan="3">Natural Language Processing</td>
+    <td>Static Quantization (IPEX)</td>
+    <td><a href="./pytorch/nlp/huggingface_models/language-modeling/quantization/static_quant/ipex">link</a></td>
+</tr>
+<tr>
+    <td>Static Quantization (PT2E)</td>
+    <td><a href="./pytorch/nlp/huggingface_models/language-modeling/quantization/static_quant/pt2e">link</a></td>
+</tr>
+<tr>
+    <td>Weight-Only Quantization</td>
+    <td><a href="./pytorch/nlp/huggingface_models/language-modeling/quantization/weight_only">link</a></td>
+</tr>
+<tr>
+    <td rowspan="2">resnet18</td>
+    <td rowspan="2">Image Recognition</td>
+    <td>Mixed Precision</td>
+    <td><a href="./pytorch/cv/mixed_precision">link</a></td>
+</tr>
+<tr>
+    <td>Static Quantization</td>
+    <td><a href="./pytorch/cv/static_quant">link</a></td>
+</tr>
+</tbody>
+</table>
+
+
+# TensorFlow Examples
+
+## Quantization
+
+<table>
+<thead>
+  <tr>
+    <th>Model</th>
+    <th>Domain</th>
+    <th>Method</th>
+    <th>Examples</th>
+  </tr>
+</thead>
+<tbody>
+ <tr>
+    <td>bert_large_squad_model_zoo</td>
+    <td>Natural Language Processing</td>
+    <td>Post-Training Static Quantization</td>
+    <td><a href="./tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq">link</a></td>
+</tr>
+<tr>
+    <td>transformer_lt</td>
+    <td>Natural Language Processing</td>
+    <td>Post-Training Static Quantization</td>
+    <td><a href="./tensorflow/nlp/transformer_lt/quantization/ptq">link</a></td>
+</tr>
+<tr>
+    <td>inception_v3</td>
+    <td>Image Recognition</td>
+    <td>Post-Training Static Quantization</td>
+    <td><a href="./tensorflow/image_recognition/inception_v3/quantization/ptq">link</a></td>
+</tr>
+<tr>
+    <td>mobilenetv2</td>
+    <td>Image Recognition</td>
+    <td>Post-Training Static Quantization</td>
+    <td><a href="./tensorflow/image_recognition/mobilenet_v2/quantization/ptq">link</a></td>
+</tr>
+<tr>
+    <td>resnetv2_50</td>
+    <td>Image Recognition</td>
+    <td>Post-Training Static Quantization</td>
+    <td><a href="./tensorflow/image_recognition/resnet_v2_50/quantization/ptq">link</a></td>
+</tr>
+<tr>
+    <td>vgg16</td>
+    <td>Image Recognition</td>
+    <td>Post-Training Static Quantization</td>
+    <td><a href="./tensorflow/image_recognition/vgg16/quantization/ptq">link</a></td>
+</tr>
+<tr>
+    <td>ViT</td>
+    <td>Image Recognition</td>
+    <td>Post-Training Static Quantization</td>
+    <td><a href="./tensorflow/image_recognition/vision_transformer/quantization/ptq">link</a></td>
+</tr>
+<tr>
+    <td>GraphSage</td>
+    <td>Graph Networks</td>
+    <td>Post-Training Static Quantization</td>
+    <td><a href="./tensorflow/graph_networks/graphsage/quantization/ptq">link</a></td>
+</tr>
+<tr>
+    <td>yolo_v5</td>
+    <td>Object Detection</td>
+    <td>Post-Training Static Quantization</td>
+    <td><a href="./tensorflow/object_detection/yolo_v5/quantization/ptq">link</a></td>
+</tr>
+<tr>
+    <td>faster_rcnn_resnet50</td>
+    <td>Object Detection</td>
+    <td>Post-Training Static Quantization</td>
+    <td><a href="./tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq">link</a></td>
+</tr>
+<tr>
+    <td>mask_rcnn_inception_v2</td>
+    <td>Object Detection</td>
+    <td>Post-Training Static Quantization</td>
+    <td><a href="./tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq">link</a></td>
+</tr>
+<tr>
+    <td>ssd_mobilenet_v1</td>
+    <td>Object Detection</td>
+    <td>Post-Training Static Quantization</td>
+    <td><a href="./tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq">link</a></td>
+</tr>
+<tr>
+    <td>wide_deep_large_ds</td>
+    <td>Recommendation</td>
+    <td>Post-Training Static Quantization</td>
+    <td><a href="./tensorflow/recommendation/wide_deep_large_ds/quantization/ptq">link</a></td>
+</tr>
+<tr>
+    <td>3dunet-mlperf</td>
+    <td>Semantic Image Segmentation</td>
+    <td>Post-Training Static Quantization</td>
+    <td><a href="./tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq">link</a></td>
+</tr>
+
+</tbody>
+</table>
+