diff --git a/modelcenter/ERNIE-3.0/fastdeploy_cn.md b/modelcenter/ERNIE-3.0/fastdeploy_cn.md
new file mode 100644
index 0000000000000000000000000000000000000000..534cd04ae56572b75dc6e620df611cd6d32f981d
--- /dev/null
+++ b/modelcenter/ERNIE-3.0/fastdeploy_cn.md
@@ -0,0 +1,49 @@
+## 0. 全场景高性能AI推理部署工具 FastDeploy
+FastDeploy 是一款**全场景、易用灵活、极致高效**的AI推理部署工具。提供开箱即用的**云边端**部署体验, 支持超过 150+ Text, Vision, Speech和跨模态模型，实现了AI模型**端到端的优化加速**。目前支持的硬件包括 **X86 CPU、NVIDIA GPU、ARM CPU、XPU、NPU、IPU**等10类云边端的硬件，通过一行代码切换不同推理后端和硬件。
+
+使用 FastDeploy 3步即可搞定AI模型部署：（1）安装FastDeploy预编译包（2）调用FastDeploy的API实现部署代码 （3）推理部署。
+
+**注** : 本文档下载 FastDeploy 示例来完成高性能部署体验；仅展示X86 CPU、NVIDIA GPU的推理，且默认已经准备好GPU环境（如 CUDA >= 11.2等），如需要部署其他硬件或者完整了解 FastDeploy 部署能力，请参考 [FastDeploy的GitHub仓库](https://github.com/PaddlePaddle/FastDeploy)
+
+
+## 1. 安装FastDeploy预编译包
+```
+pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
+```
+## 2. 运行部署示例
+```
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/text/ernie-3.0/python
+
+# 下载AFQMC数据集的微调后的ERNIE 3.0模型
+wget https://bj.bcebos.com/fastdeploy/models/ernie-3.0/ernie-3.0-medium-zh-afqmc.tgz
+tar xvfz ernie-3.0-medium-zh-afqmc.tgz
+
+# CPU 推理
+python seq_cls_infer.py --device cpu --model_dir ernie-3.0-medium-zh-afqmc
+
+# GPU 推理
+python seq_cls_infer.py --device gpu --model_dir ernie-3.0-medium-zh-afqmc
+```
+运行完成后返回的结果如下：
+
+```bash
+[INFO] fastdeploy/runtime.cc(469)::Init Runtime initialized with Backend::ORT in Device::CPU.
+Batch id:0, example id:0, sentence1:花呗收款额度限制, sentence2:收钱码，对花呗支付的金额有限制吗, label:1, similarity:0.5819
+Batch id:1, example id:0, sentence1:花呗支持高铁票支付吗, sentence2:为什么友付宝不支持花呗付款, label:0, similarity:0.9979
+```
+
+### 参数说明
+
+`seq_cls_infer.py` 除了以上示例的命令行参数，还支持更多命令行参数的设置。以下为各命令行参数的说明。
+
+| 参数 |参数说明 |
+|----------|--------------|
+|--model_dir | 指定部署模型的目录， |
+|--batch_size |最大可测的 batch size，默认为 1|
+|--max_length |最大序列长度，默认为 128|
+|--device | 运行的设备，可选范围: ['cpu', 'gpu']，默认为'cpu' |
+|--backend | 支持的推理后端，可选范围: ['onnx_runtime', 'paddle', 'openvino', 'tensorrt', 'paddle_tensorrt']，默认为'onnx_runtime' |
+|--use_fp16 | 是否使用FP16模式进行推理。使用tensorrt和paddle_tensorrt后端时可开启，默认为False |
+|--use_fast| 是否使用FastTokenizer加速分词阶段。默认为True|
\ No newline at end of file
diff --git a/modelcenter/ERNIE-3.0/fastdeploy_en.md b/modelcenter/ERNIE-3.0/fastdeploy_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..4ed92be9c196c9930044f5e065f09c49d673e892
--- /dev/null
+++ b/modelcenter/ERNIE-3.0/fastdeploy_en.md
@@ -0,0 +1,50 @@
+## 0. FastDeploy
+
+FastDeploy is an Easy-to-use and High Performance AI model deployment toolkit for Cloud, Mobile and Edge with out-of-the-box and unified experience, end-to-end optimization for over 150+ Text, Vision, Speech and Cross-modal AI models. FastDeploy Supports AI model deployment on
+**X86 CPU、NVIDIA GPU、ARM CPU、XPU、NPU、IPU** etc. You can switch different inference backends and hardware with a single line of code.
+
+Deploying AI model in 3 steps with FastDeploy: (1)Install FastDeploy SDK; (2)Use FastDeploy's API to implement the deployment code; (3) Deploy.
+
+**Notes** : This document downloads FastDeploy examples to complete the high performance deployment experience; only X86 CPUs, NVIDIA GPUs are shown for reasoning and GPU environments are ready by default (e.g. CUDA >= 11.2, etc.), if you need to deploy AI model on other hardware or learn about FastDeploy's full capabilities, please refer to [FastDeploy GitHub](https://github.com/PaddlePaddle/FastDeploy).
+
+## 1. Install FastDeploy SDK
+```
+pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
+```
+## 2. Run Deployment Example
+```
+# download deployment example
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/text/ernie-3.0/python
+
+# download the fine-tuned ERNIE 3.0 model trained from the AFQMC dataset
+wget https://bj.bcebos.com/fastdeploy/models/ernie-3.0/ernie-3.0-medium-zh-afqmc.tgz
+tar xvfz ernie-3.0-medium-zh-afqmc.tgz
+
+# CPU deployment
+python seq_cls_infer.py --device cpu --model_dir ernie-3.0-medium-zh-afqmc
+
+# GPU deployment
+python seq_cls_infer.py --device gpu --model_dir ernie-3.0-medium-zh-afqmc
+```
+The results returned after the operation is completed are as follows:
+
+```bash
+[INFO] fastdeploy/runtime.cc(469)::Init Runtime initialized with Backend::ORT in Device::CPU.
+Batch id:0, example id:0, sentence1:花呗收款额度限制, sentence2:收钱码，对花呗支付的金额有限制吗, label:1, similarity:0.5819
+Batch id:1, example id:0, sentence1:花呗支持高铁票支付吗, sentence2:为什么友付宝不支持花呗付款, label:0, similarity:0.9979
+```
+
+### Parameter Description
+
+`seq_cls_infer.py` In addition to the command line parameters in the above example, more command line parameters are also supported. The following is a description of each command line parameter.
+
+| Parameter |Parameter Description |
+|----------|--------------|
+|--model_dir | Specify the directory where the model is deployed， |
+|--batch_size |Maximum measurable batch size，default 1|
+|--max_length |Maximum sequence length，default 128|
+|--device | equipment running，Optional range: ['cpu', 'gpu']，default'cpu' |
+|--backend | Supported Inference Backends，Optional range: ['onnx_runtime', 'paddle', 'openvino', 'tensorrt', 'paddle_tensorrt']，default 'onnx_runtime' |
+|--use_fp16 | Whether to use FP16 mode for inference。Use tensorrt and paddle_tensorrt can be turned on when backend，default False |
+|--use_fast| Whether to use FastTokenizer to speed up the word segmentation stage。default True|
\ No newline at end of file
diff --git a/modelcenter/PP-HGNet/fastdeploy_cn.md b/modelcenter/PP-HGNet/fastdeploy_cn.md
new file mode 100644
index 0000000000000000000000000000000000000000..bd38bf0af3eaa8001b962e7ce18d157cfc5fe16e
--- /dev/null
+++ b/modelcenter/PP-HGNet/fastdeploy_cn.md
@@ -0,0 +1,40 @@
+## 0. 全场景高性能AI推理部署工具 FastDeploy
+FastDeploy 是一款**全场景、易用灵活、极致高效**的AI推理部署工具。提供开箱即用的**云边端**部署体验, 支持超过 150+ Text, Vision, Speech和跨模态模型，实现了AI模型**端到端的优化加速**。目前支持的硬件包括 **X86 CPU、NVIDIA GPU、ARM CPU、XPU、NPU、IPU**等10类云边端的硬件，通过一行代码切换不同推理后端和硬件。
+
+使用 FastDeploy 3步即可搞定AI模型部署：（1）安装FastDeploy预编译包（2）调用FastDeploy的API实现部署代码 （3）推理部署。
+
+**注** : 本文档下载 FastDeploy 示例来完成高性能部署体验；仅展示X86 CPU、NVIDIA GPU的推理，且默认已经准备好GPU环境（如 CUDA >= 11.2等），如需要部署其他硬件或者完整了解 FastDeploy 部署能力，请参考 [FastDeploy的GitHub仓库](https://github.com/PaddlePaddle/FastDeploy)
+
+
+## 1. 安装FastDeploy预编译包
+```
+pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
+```
+## 2. 运行部署示例
+```
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/classification/paddleclas/python
+
+# 下载HGNet模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_tiny_ssld_infer.tgz
+tar xvfz PPHGNet_tiny_ssld_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# CPU推理
+python infer.py --model PPHGNet_tiny_ssld_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --topk 1
+# GPU推理
+python infer.py --model PPHGNet_tiny_ssld_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --topk 1
+# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
+python infer.py --model PPHGNet_tiny_ssld_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True --topk 1
+# IPU推理（注意：IPU推理首次运行会有序列化模型的操作，有一定耗时，需要耐心等待）
+python infer.py --model PPHGNet_tiny_ssld_infer --image ILSVRC2012_val_00000010.jpeg --device ipu --topk 1
+
+运行完成后返回的结果如下：
+
+```bash
+==============================PPHGNet_tiny_ssld==============================
+cpu_label: 153, cpu_score: 0.536040
+ipu_label: 153, ipu_score: 0.536039
+==============================PPHGNet_tiny_ssld==============================
+```
\ No newline at end of file
diff --git a/modelcenter/PP-HGNet/fastdeploy_en.md b/modelcenter/PP-HGNet/fastdeploy_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..a10028afdf47d7049461796b141ecb1fd8c25f5b
--- /dev/null
+++ b/modelcenter/PP-HGNet/fastdeploy_en.md
@@ -0,0 +1,42 @@
+## 0. FastDeploy
+
+FastDeploy is an Easy-to-use and High Performance AI model deployment toolkit for Cloud, Mobile and Edge with out-of-the-box and unified experience, end-to-end optimization for over 150+ Text, Vision, Speech and Cross-modal AI models. FastDeploy Supports AI model deployment on
+**X86 CPU、NVIDIA GPU、ARM CPU、XPU、NPU、IPU** etc. You can switch different inference backends and hardware with a single line of code.
+
+Deploying AI model in 3 steps with FastDeploy: (1)Install FastDeploy SDK; (2)Use FastDeploy's API to implement the deployment code; (3) Deploy.
+
+**Notes** : This document downloads FastDeploy examples to complete the high performance deployment experience; only X86 CPUs, NVIDIA GPUs are shown for reasoning and GPU environments are ready by default (e.g. CUDA >= 11.2, etc.), if you need to deploy AI model on other hardware or learn about FastDeploy's full capabilities, please refer to [FastDeploy GitHub](https://github.com/PaddlePaddle/FastDeploy).
+
+## 1. Install FastDeploy SDK
+```
+pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
+```
+## 2. Run Deployment Example
+```
+# download deployment example
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/classification/paddleclas/python
+
+# download HGNet model and test image
+wget https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_tiny_ssld_infer.tgz
+tar xvfz PPHGNet_tiny_ssld_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# CPU deployment
+python infer.py --model PPHGNet_tiny_ssld_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --topk 1
+# GPU deployment
+python infer.py --model PPHGNet_tiny_ssld_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --topk 1
+#TensorRT inference on GPU (note: if you run TensorRT inference the first time, there is a serialization of the model, which is time-consuming and requires patience)
+python infer.py --model PPHGNet_tiny_ssld_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True --topk 1
+#IPU inference (note: the first run of IPU inference will have serialized model operations, which will take a certain amount of time, so you need to wait patiently)
+python infer.py --model PPHGNet_tiny_ssld_infer --image ILSVRC2012_val_00000010.jpeg --device ipu --topk 1
+```
+
+The results returned after the operation is completed are as follows：
+
+```bash
+==============================PPHGNet_tiny_ssld==============================
+cpu_label: 153, cpu_score: 0.536040
+ipu_label: 153, ipu_score: 0.536039
+==============================PPHGNet_tiny_ssld==============================
+```
\ No newline at end of file
diff --git a/modelcenter/PP-HumanSegV2/fastdeploy_cn.md b/modelcenter/PP-HumanSegV2/fastdeploy_cn.md
new file mode 100644
index 0000000000000000000000000000000000000000..2445b2c3825bcedc83455523f90fd68c5c42e6ab
--- /dev/null
+++ b/modelcenter/PP-HumanSegV2/fastdeploy_cn.md
@@ -0,0 +1,30 @@
+## 0. 全场景高性能AI推理部署工具 FastDeploy
+FastDeploy 是一款**全场景、易用灵活、极致高效**的AI推理部署工具。提供开箱即用的**云边端**部署体验, 支持超过 150+ Text, Vision, Speech和跨模态模型，实现了AI模型**端到端的优化加速**。目前支持的硬件包括 **X86 CPU、NVIDIA GPU、ARM CPU、XPU、NPU、IPU**等10类云边端的硬件，通过一行代码切换不同推理后端和硬件。
+
+使用 FastDeploy 3步即可搞定AI模型部署：（1）安装FastDeploy预编译包（2）调用FastDeploy的API实现部署代码 （3）推理部署。
+
+**注** : 本文档下载 FastDeploy 示例来完成高性能部署体验；仅展示X86 CPU、NVIDIA GPU的推理，且默认已经准备好GPU环境（如 CUDA >= 11.2等），如需要部署其他硬件或者完整了解 FastDeploy 部署能力，请参考 [FastDeploy的GitHub仓库](https://github.com/PaddlePaddle/FastDeploy)
+
+
+## 1. 安装FastDeploy预编译包
+```
+pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
+```
+## 2. 运行部署示例
+```
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/segmentation/paddleseg/python
+
+# 下载HumanSegV2模型文件和测试图片
+wget https://bj.bcebos.com/paddle2onnx/libs/PP_HumanSegV2_Lite_192x192_infer.tgz
+tar -xvf PP_HumanSegV2_Lite_192x192_infer.tgz
+wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
+
+# CPU推理
+python infer.py --model PP_HumanSegV2_Lite_192x192_infer --image cityscapes_demo.png --device cpu
+# GPU推理
+python infer.py --model PP_HumanSegV2_Lite_192x192_infer --image cityscapes_demo.png --device gpu
+# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
+python infer.py --model PP_HumanSegV2_Lite_192x192_infer --image cityscapes_demo.png --device gpu --use_trt True
+```
\ No newline at end of file
diff --git a/modelcenter/PP-HumanSegV2/fastdeploy_en.md b/modelcenter/PP-HumanSegV2/fastdeploy_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..035b8c40d3a00f6e36a8a2a21d2a22a5fa320b3f
--- /dev/null
+++ b/modelcenter/PP-HumanSegV2/fastdeploy_en.md
@@ -0,0 +1,33 @@
+## 0. FastDeploy
+
+FastDeploy is an Easy-to-use and High Performance AI model deployment toolkit for Cloud, Mobile and Edge with out-of-the-box and unified experience, end-to-end optimization for over 150+ Text, Vision, Speech and Cross-modal AI models. FastDeploy Supports AI model deployment on
+**X86 CPU、NVIDIA GPU、ARM CPU、XPU、NPU、IPU** etc. You can switch different inference backends and hardware with a single line of code.
+
+Deploying AI model in 3 steps with FastDeploy: (1)Install FastDeploy SDK; (2)Use FastDeploy's API to implement the deployment code; (3) Deploy.
+
+**Notes** : This document downloads FastDeploy examples to complete the high performance deployment experience; only X86 CPUs, NVIDIA GPUs are shown for reasoning and GPU environments are ready by default (e.g. CUDA >= 11.2, etc.), if you need to deploy AI model on other hardware or learn about FastDeploy's full capabilities, please refer to [FastDeploy GitHub](https://github.com/PaddlePaddle/FastDeploy).
+
+## 1. Install FastDeploy SDK
+```
+pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
+```
+## 2. Run Deployment Example
+```
+# download deployment example
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/segmentation/paddleseg/python
+
+# download HumanSegV2 model and test image
+wget https://bj.bcebos.com/paddle2onnx/libs/PP_HumanSegV2_Lite_192x192_infer.tgz
+tar -xvf PP_HumanSegV2_Lite_192x192_infer.tgz
+wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
+
+# CPU deployment
+python infer.py --model PP_HumanSegV2_Lite_192x192_infer --image cityscapes_demo.png --device cpu
+
+# GPU deployment
+python infer.py --model PP_HumanSegV2_Lite_192x192_infer --image cityscapes_demo.png --device gpu
+
+#TensorRT inference on GPU (note: if you run TensorRT inference the first time, there is a serialization of the model, which is time-consuming and requires patience)
+python infer.py --model PP_HumanSegV2_Lite_192x192_infer --image cityscapes_demo.png --device gpu --use_trt True
+```
\ No newline at end of file
diff --git a/modelcenter/PP-LCNet/fastdeploy_cn.md b/modelcenter/PP-LCNet/fastdeploy_cn.md
new file mode 100644
index 0000000000000000000000000000000000000000..94f5bc6ca1596b5f69fe9d90d28fedf98125dc80
--- /dev/null
+++ b/modelcenter/PP-LCNet/fastdeploy_cn.md
@@ -0,0 +1,41 @@
+## 0. 全场景高性能AI推理部署工具 FastDeploy
+FastDeploy 是一款**全场景、易用灵活、极致高效**的AI推理部署工具。提供开箱即用的**云边端**部署体验, 支持超过 150+ Text, Vision, Speech和跨模态模型，实现了AI模型**端到端的优化加速**。目前支持的硬件包括 **X86 CPU、NVIDIA GPU、ARM CPU、XPU、NPU、IPU**等10类云边端的硬件，通过一行代码切换不同推理后端和硬件。
+
+使用 FastDeploy 3步即可搞定AI模型部署：（1）安装FastDeploy预编译包（2）调用FastDeploy的API实现部署代码 （3）推理部署。
+
+**注** : 本文档下载 FastDeploy 示例来完成高性能部署体验；仅展示X86 CPU、NVIDIA GPU的推理，且默认已经准备好GPU环境（如 CUDA >= 11.2等），如需要部署其他硬件或者完整了解 FastDeploy 部署能力，请参考 [FastDeploy的GitHub仓库](https://github.com/PaddlePaddle/FastDeploy)
+
+
+## 1. 安装FastDeploy预编译包
+```
+pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
+```
+## 2. 运行部署示例
+```
+#下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/classification/paddleclas/python
+
+# 下载LCNet模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNet_x1_0_infer.tgz
+tar -xvf PPLCNet_x1_0_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# CPU推理
+python infer.py --model PPLCNet_x1_0_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --topk 1
+# GPU推理
+python infer.py --model PPLCNet_x1_0_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --topk 1
+# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
+python infer.py --model PPLCNet_x1_0_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True --topk 1
+# IPU推理（注意：IPU推理首次运行会有序列化模型的操作，有一定耗时，需要耐心等待）
+python infer.py --model PPLCNet_x1_0_infer --image ILSVRC2012_val_00000010.jpeg --device ipu --topk 1
+```
+
+运行完成后返回的结果如下：
+
+```bash
+==============================PPLCNet_x1_0==============================
+cpu_label: 153, cpu_score: 0.612086
+ipu_label: 153, ipu_score: 0.612087
+==============================PPLCNet_x1_0==============================
+```
\ No newline at end of file
diff --git a/modelcenter/PP-LCNet/fastdeploy_en.md b/modelcenter/PP-LCNet/fastdeploy_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..04700ef3c0b3701fcb792867432bb6c50406cf5d
--- /dev/null
+++ b/modelcenter/PP-LCNet/fastdeploy_en.md
@@ -0,0 +1,42 @@
+## 0. FastDeploy
+
+FastDeploy is an Easy-to-use and High Performance AI model deployment toolkit for Cloud, Mobile and Edge with out-of-the-box and unified experience, end-to-end optimization for over 150+ Text, Vision, Speech and Cross-modal AI models. FastDeploy Supports AI model deployment on
+**X86 CPU、NVIDIA GPU、ARM CPU、XPU、NPU、IPU** etc. You can switch different inference backends and hardware with a single line of code.
+
+Deploying AI model in 3 steps with FastDeploy: (1)Install FastDeploy SDK; (2)Use FastDeploy's API to implement the deployment code; (3) Deploy.
+
+**Notes** : This document downloads FastDeploy examples to complete the high performance deployment experience; only X86 CPUs, NVIDIA GPUs are shown for reasoning and GPU environments are ready by default (e.g. CUDA >= 11.2, etc.), if you need to deploy AI model on other hardware or learn about FastDeploy's full capabilities, please refer to [FastDeploy GitHub](https://github.com/PaddlePaddle/FastDeploy).
+
+## 1. Install FastDeploy SDK
+```
+pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
+```
+## 2. Run Deployment Example
+```
+# download deployment example
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/classification/paddleclas/python
+
+# download LCNet model and test image
+wget https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNet_x1_0_infer.tgz
+tar -xvf PPLCNet_x1_0_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# CPU deployment
+python infer.py --model PPLCNet_x1_0_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --topk 1
+# GPU deployment
+python infer.py --model PPLCNet_x1_0_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --topk 1
+#TensorRT inference on GPU (note: if you run TensorRT inference the first time, there is a serialization of the model, which is time-consuming and requires patience)
+python infer.py --model PPLCNet_x1_0_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True --topk 1
+#IPU inference (note: the first run of IPU inference will have serialized model operations, which will take a certain amount of time, so you need to wait patiently)
+python infer.py --model PPLCNet_x1_0_infer --image ILSVRC2012_val_00000010.jpeg --device ipu --topk 1
+```
+
+The results returned after the operation is completed are as follows：
+
+```bash
+==============================PPLCNet_x1_0==============================
+cpu_label: 153, cpu_score: 0.612086
+ipu_label: 153, ipu_score: 0.612087
+==============================PPLCNet_x1_0==============================
+```
\ No newline at end of file
diff --git a/modelcenter/PP-LCNetV2/fastdeploy_cn.md b/modelcenter/PP-LCNetV2/fastdeploy_cn.md
new file mode 100644
index 0000000000000000000000000000000000000000..da6229738dad46298f14693c25f9d7ec87cd25b9
--- /dev/null
+++ b/modelcenter/PP-LCNetV2/fastdeploy_cn.md
@@ -0,0 +1,41 @@
+## 0. 全场景高性能AI推理部署工具 FastDeploy
+FastDeploy 是一款**全场景、易用灵活、极致高效**的AI推理部署工具。提供开箱即用的**云边端**部署体验, 支持超过 150+ Text, Vision, Speech和跨模态模型，实现了AI模型**端到端的优化加速**。目前支持的硬件包括 **X86 CPU、NVIDIA GPU、ARM CPU、XPU、NPU、IPU**等10类云边端的硬件，通过一行代码切换不同推理后端和硬件。
+
+使用 FastDeploy 3步即可搞定AI模型部署：（1）安装FastDeploy预编译包（2）调用FastDeploy的API实现部署代码 （3）推理部署。
+
+**注** : 本文档下载 FastDeploy 示例来完成高性能部署体验；仅展示X86 CPU、NVIDIA GPU的推理，且默认已经准备好GPU环境（如 CUDA >= 11.2等），如需要部署其他硬件或者完整了解 FastDeploy 部署能力，请参考 [FastDeploy的GitHub仓库](https://github.com/PaddlePaddle/FastDeploy)
+
+
+## 1. 安装FastDeploy预编译包
+```
+pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
+```
+## 2. 运行部署示例
+```
+#下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/classification/paddleclas/python
+
+# 下载LCNetv2模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNetV2_base_infer.tgz
+tar -xvf PPLCNetV2_base_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# CPU推理
+python infer.py --model PPLCNetV2_base_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --topk 1
+# GPU推理
+python infer.py --model PPLCNetV2_base_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --topk 1
+# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
+python infer.py --model PPLCNetV2_base_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True --topk 1
+# IPU推理（注意：IPU推理首次运行会有序列化模型的操作，有一定耗时，需要耐心等待）
+python infer.py --model PPLCNetV2_base_infer --image ILSVRC2012_val_00000010.jpeg --device ipu --topk 1
+```
+
+运行完成后返回的结果如下：
+
+```bash
+==============================PPLCNetV2_base==============================
+cpu_label: 332, cpu_score: 0.278354
+ipu_label: 332, ipu_score: 0.278357
+==============================PPLCNetV2_base==============================
+```
\ No newline at end of file
diff --git a/modelcenter/PP-LCNetV2/fastdeploy_en.md b/modelcenter/PP-LCNetV2/fastdeploy_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..f82a680dc712c23a6ea62123413656d406cd296a
--- /dev/null
+++ b/modelcenter/PP-LCNetV2/fastdeploy_en.md
@@ -0,0 +1,42 @@
+## 0. FastDeploy
+
+FastDeploy is an Easy-to-use and High Performance AI model deployment toolkit for Cloud, Mobile and Edge with out-of-the-box and unified experience, end-to-end optimization for over 150+ Text, Vision, Speech and Cross-modal AI models. FastDeploy Supports AI model deployment on
+**X86 CPU、NVIDIA GPU、ARM CPU、XPU、NPU、IPU** etc. You can switch different inference backends and hardware with a single line of code.
+
+Deploying AI model in 3 steps with FastDeploy: (1)Install FastDeploy SDK; (2)Use FastDeploy's API to implement the deployment code; (3) Deploy.
+
+**Notes** : This document downloads FastDeploy examples to complete the high performance deployment experience; only X86 CPUs, NVIDIA GPUs are shown for reasoning and GPU environments are ready by default (e.g. CUDA >= 11.2, etc.), if you need to deploy AI model on other hardware or learn about FastDeploy's full capabilities, please refer to [FastDeploy GitHub](https://github.com/PaddlePaddle/FastDeploy).
+
+## 1. Install FastDeploy SDK
+```
+pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
+```
+## 2. Run Deployment Example
+```
+# download deployment example
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/classification/paddleclas/python
+
+# download LCNetv2 model and test image
+wget https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNetV2_base_infer.tgz
+tar -xvf PPLCNetV2_base_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# CPU deployment
+python infer.py --model PPLCNetV2_base_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --topk 1
+# GPU deployment
+python infer.py --model PPLCNetV2_base_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --topk 1
+#TensorRT inference on GPU (note: if you run TensorRT inference the first time, there is a serialization of the model, which is time-consuming and requires patience)
+python infer.py --model PPLCNetV2_base_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True --topk 1
+#IPU inference (note: the first run of IPU inference will have serialized model operations, which will take a certain amount of time, so you need to wait patiently)
+python infer.py --model PPLCNetV2_base_infer --image ILSVRC2012_val_00000010.jpeg --device ipu --topk 1
+```
+
+The results returned after the operation is completed are as follows：
+
+```bash
+==============================PPLCNetV2_base==============================
+cpu_label: 332, cpu_score: 0.278354
+ipu_label: 332, ipu_score: 0.278357
+==============================PPLCNetV2_base==============================
+```
\ No newline at end of file
diff --git a/modelcenter/PP-MSVSR/fastdeploy_cn.md b/modelcenter/PP-MSVSR/fastdeploy_cn.md
new file mode 100644
index 0000000000000000000000000000000000000000..783c244272f00d6e49258ed6f7d3f9d72a09984d
--- /dev/null
+++ b/modelcenter/PP-MSVSR/fastdeploy_cn.md
@@ -0,0 +1,34 @@
+## 0. 全场景高性能AI推理部署工具 FastDeploy
+FastDeploy 是一款**全场景、易用灵活、极致高效**的AI推理部署工具。提供开箱即用的**云边端**部署体验, 支持超过 150+ Text, Vision, Speech和跨模态模型，实现了AI模型**端到端的优化加速**。目前支持的硬件包括 **X86 CPU、NVIDIA GPU、ARM CPU、XPU、NPU、IPU**等10类云边端的硬件，通过一行代码切换不同推理后端和硬件。
+
+使用 FastDeploy 3步即可搞定AI模型部署：（1）安装FastDeploy预编译包（2）调用FastDeploy的API实现部署代码 （3）推理部署。
+
+**注** : 本文档下载 FastDeploy 示例来完成高性能部署体验；仅展示X86 CPU、NVIDIA GPU的推理，且默认已经准备好GPU环境（如 CUDA >= 11.2等），如需要部署其他硬件或者完整了解 FastDeploy 部署能力，请参考 [FastDeploy的GitHub仓库](https://github.com/PaddlePaddle/FastDeploy)
+
+
+## 1. 安装FastDeploy预编译包
+```
+pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
+```
+## 2. 运行部署示例
+```
+#下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/sr/ppmsvsr/python
+
+# 下载VSR模型文件和测试视频
+wget https://bj.bcebos.com/paddlehub/fastdeploy/PP-MSVSR_reds_x4.tar
+tar -xvf PP-MSVSR_reds_x4.tar
+wget https://bj.bcebos.com/paddlehub/fastdeploy/vsr_src.mp4
+# CPU推理
+python infer.py --model PP-MSVSR_reds_x4 --video person.mp4 --frame_num 2 --device cpu
+# GPU推理
+python infer.py --model PP-MSVSR_reds_x4 --video person.mp4 --frame_num 2 --device gpu
+# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
+python infer.py --model PP-MSVSR_reds_x4 --video person.mp4 --frame_num 2 --device gpu --use_trt True
+```
+
+运行完成可视化结果如下图所示:
+
+
+
+
+
\ No newline at end of file
diff --git a/modelcenter/PP-OCRv2/fastdeploy_en.md b/modelcenter/PP-OCRv2/fastdeploy_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..bc1cc60e1a0b99acb1867781bbaa2e957b899441
--- /dev/null
+++ b/modelcenter/PP-OCRv2/fastdeploy_en.md
@@ -0,0 +1,45 @@
+## 0. FastDeploy
+
+FastDeploy is an Easy-to-use and High Performance AI model deployment toolkit for Cloud, Mobile and Edge with out-of-the-box and unified experience, end-to-end optimization for over 150+ Text, Vision, Speech and Cross-modal AI models. FastDeploy Supports AI model deployment on
+**X86 CPU、NVIDIA GPU、ARM CPU、XPU、NPU、IPU** etc. You can switch different inference backends and hardware with a single line of code.
+
+Deploying AI model in 3 steps with FastDeploy: (1)Install FastDeploy SDK; (2)Use FastDeploy's API to implement the deployment code; (3) Deploy.
+
+**Notes** : This document downloads FastDeploy examples to complete the high performance deployment experience; only X86 CPUs, NVIDIA GPUs are shown for reasoning and GPU environments are ready by default (e.g. CUDA >= 11.2, etc.), if you need to deploy AI model on other hardware or learn about FastDeploy's full capabilities, please refer to [FastDeploy GitHub](https://github.com/PaddlePaddle/FastDeploy).
+
+## 1. Install FastDeploy SDK
+```
+pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
+```
+## 2. Run Deployment Example
+```
+# download model, image and dictionary files
+wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
+tar -xvf ch_PP-OCRv2_det_infer.tar
+
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar
+
+wgethttps://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
+tar -xvf ch_PP-OCRv2_rec_infer.tar
+
+wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
+
+wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
+
+
+# download deployment example
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd examples/vison/ocr/PP-OCRv2/python/
+
+
+# CPU deployment
+python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device cpu
+# GPU deployment
+python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu
+# TensorRT inference on GPU (note: if you run TensorRT inference the first time, there is a serialization of the model, which is time-consuming and requires patience)
+python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend trt
+```
+
+The results of the completed visualisation are shown below
+
\ No newline at end of file
diff --git a/modelcenter/PP-OCRv3/fastdeploy_cn.md b/modelcenter/PP-OCRv3/fastdeploy_cn.md
new file mode 100644
index 0000000000000000000000000000000000000000..1d6ebb9e7c38cbdabbc88dcd9c39053d6bfcce43
--- /dev/null
+++ b/modelcenter/PP-OCRv3/fastdeploy_cn.md
@@ -0,0 +1,42 @@
+## 0. 全场景高性能AI推理部署工具 FastDeploy
+FastDeploy 是一款**全场景、易用灵活、极致高效**的AI推理部署工具。提供开箱即用的**云边端**部署体验, 支持超过 150+ Text, Vision, Speech和跨模态模型，实现了AI模型**端到端的优化加速**。目前支持的硬件包括 **X86 CPU、NVIDIA GPU、ARM CPU、XPU、NPU、IPU**等10类云边端的硬件，通过一行代码切换不同推理后端和硬件。
+
+使用 FastDeploy 3步即可搞定AI模型部署：（1）安装FastDeploy预编译包（2）调用FastDeploy的API实现部署代码 （3）推理部署。
+
+**注** : 本文档下载 FastDeploy 示例来完成高性能部署体验；仅展示X86 CPU、NVIDIA GPU的推理，且默认已经准备好GPU环境（如 CUDA >= 11.2等），如需要部署其他硬件或者完整了解 FastDeploy 部署能力，请参考 [FastDeploy的GitHub仓库](https://github.com/PaddlePaddle/FastDeploy)
+
+
+## 1. 安装FastDeploy预编译包
+```
+pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
+```
+## 2. 运行部署示例
+```
+# 下载模型,图片和字典文件
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
+tar xvf ch_PP-OCRv3_det_infer.tar
+
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar
+
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
+tar xvf ch_PP-OCRv3_rec_infer.tar
+
+wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
+
+wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
+
+#下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd examples/vison/ocr/PP-OCRv3/python/
+
+# CPU推理
+python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device cpu
+# GPU推理
+python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu
+# GPU上使用TensorRT推理
+python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend trt
+```
+
+运行完成可视化结果如下图所示
+
\ No newline at end of file
diff --git a/modelcenter/PP-OCRv3/fastdeploy_en.md b/modelcenter/PP-OCRv3/fastdeploy_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..f86e9ac253eafff388e01c2174ba0d22dfa6683f
--- /dev/null
+++ b/modelcenter/PP-OCRv3/fastdeploy_en.md
@@ -0,0 +1,44 @@
+## 0. FastDeploy
+
+FastDeploy is an Easy-to-use and High Performance AI model deployment toolkit for Cloud, Mobile and Edge with out-of-the-box and unified experience, end-to-end optimization for over 150+ Text, Vision, Speech and Cross-modal AI models. FastDeploy Supports AI model deployment on
+**X86 CPU、NVIDIA GPU、ARM CPU、XPU、NPU、IPU** etc. You can switch different inference backends and hardware with a single line of code.
+
+Deploying AI model in 3 steps with FastDeploy: (1)Install FastDeploy SDK; (2)Use FastDeploy's API to implement the deployment code; (3) Deploy.
+
+**Notes** : This document downloads FastDeploy examples to complete the high performance deployment experience; only X86 CPUs, NVIDIA GPUs are shown for reasoning and GPU environments are ready by default (e.g. CUDA >= 11.2, etc.), if you need to deploy AI model on other hardware or learn about FastDeploy's full capabilities, please refer to [FastDeploy GitHub](https://github.com/PaddlePaddle/FastDeploy).
+
+## 1. Install FastDeploy SDK
+```
+pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
+```
+## 2. Run Deployment Example
+```
+# download model, image and dictionary files
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
+tar xvf ch_PP-OCRv3_det_infer.tar
+
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar
+
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
+tar xvf ch_PP-OCRv3_rec_infer.tar
+
+wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
+
+wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
+
+# download deployment example
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd examples/vison/ocr/PP-OCRv3/python/
+
+
+# CPU deployment
+python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device cpu
+# GPU deployment
+python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu
+# TensorRT inference on GPU (note: if you run TensorRT inference the first time, there is a serialization of the model, which is time-consuming and requires patience)
+python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend trt
+```
+
+The results of the completed visualisation are shown below
+
\ No newline at end of file
diff --git a/modelcenter/PP-PicoDet/fastdeploy_cn.md b/modelcenter/PP-PicoDet/fastdeploy_cn.md
new file mode 100644
index 0000000000000000000000000000000000000000..ec134279f3762621efde3a8a28cffb1bf0e418bc
--- /dev/null
+++ b/modelcenter/PP-PicoDet/fastdeploy_cn.md
@@ -0,0 +1,30 @@
+## 0. 全场景高性能AI推理部署工具 FastDeploy
+FastDeploy 是一款**全场景、易用灵活、极致高效**的AI推理部署工具。提供开箱即用的**云边端**部署体验, 支持超过 150+ Text, Vision, Speech和跨模态模型，实现了AI模型**端到端的优化加速**。目前支持的硬件包括 **X86 CPU、NVIDIA GPU、ARM CPU、XPU、NPU、IPU**等10类云边端的硬件，通过一行代码切换不同推理后端和硬件。
+
+使用 FastDeploy 3步即可搞定AI模型部署：（1）安装FastDeploy预编译包（2）调用FastDeploy的API实现部署代码 （3）推理部署。
+
+**注** : 本文档下载 FastDeploy 示例来完成高性能部署体验；仅展示X86 CPU、NVIDIA GPU的推理，且默认已经准备好GPU环境（如 CUDA >= 11.2等），如需要部署其他硬件或者完整了解 FastDeploy 部署能力，请参考 [FastDeploy的GitHub仓库](https://github.com/PaddlePaddle/FastDeploy)
+
+
+## 1. 安装FastDeploy预编译包
+```
+pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
+```
+## 2. 运行部署示例
+```
+#下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/detection/paddledetection/python/
+
+#下载PPYOLOE模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/picodet_l_320_coco_lcnet.tgz
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+tar xvf picodet_l_320_coco_lcnet.tgz
+
+# CPU推理
+python infer_picodet.py --model_dir picodet_l_320_coco_lcnet --image 000000014439.jpg --device cpu
+# GPU推理
+python infer_picodet.py --model_dir picodet_l_320_coco_lcnet --image 000000014439.jpg --device gpu
+# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
+python infer_picodet.py --model_dir picodet_l_320_coco_lcnet --image 000000014439.jpg --device gpu --use_trt True
+```
\ No newline at end of file
diff --git a/modelcenter/PP-PicoDet/fastdeploy_en.md b/modelcenter/PP-PicoDet/fastdeploy_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..71a5d21c3db78cbc1f5893045a85a21b55ce36f5
--- /dev/null
+++ b/modelcenter/PP-PicoDet/fastdeploy_en.md
@@ -0,0 +1,31 @@
+## 0. FastDeploy
+
+FastDeploy is an Easy-to-use and High Performance AI model deployment toolkit for Cloud, Mobile and Edge with out-of-the-box and unified experience, end-to-end optimization for over 150+ Text, Vision, Speech and Cross-modal AI models. FastDeploy Supports AI model deployment on
+**X86 CPU、NVIDIA GPU、ARM CPU、XPU、NPU、IPU** etc. You can switch different inference backends and hardware with a single line of code.
+
+Deploying AI model in 3 steps with FastDeploy: (1)Install FastDeploy SDK; (2)Use FastDeploy's API to implement the deployment code; (3) Deploy.
+
+**Notes** : This document downloads FastDeploy examples to complete the high performance deployment experience; only X86 CPUs, NVIDIA GPUs are shown for reasoning and GPU environments are ready by default (e.g. CUDA >= 11.2, etc.), if you need to deploy AI model on other hardware or learn about FastDeploy's full capabilities, please refer to [FastDeploy GitHub](https://github.com/PaddlePaddle/FastDeploy).
+
+## 1. Install FastDeploy SDK
+```
+pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
+```
+## 2. Run Deployment Example
+```
+# download deployment example
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/detection/paddledetection/python/
+
+# download PicoDet model and test image
+wget https://bj.bcebos.com/paddlehub/fastdeploy/picodet_l_320_coco_lcnet.tgz
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+tar xvf picodet_l_320_coco_lcnet.tgz
+
+# CPU deployment
+python infer_picodet.py --model_dir picodet_l_320_coco_lcnet --image 000000014439.jpg --device cpu
+# GPU deployment
+python infer_picodet.py --model_dir picodet_l_320_coco_lcnet --image 000000014439.jpg --device gpu
+# TensorRT inference on GPU (note: if you run TensorRT inference the first time, there is a serialization of the model, which is time-consuming and requires patience)
+python infer_picodet.py --model_dir picodet_l_320_coco_lcnet --image 000000014439.jpg --device gpu --use_trt True
+```
\ No newline at end of file
diff --git a/modelcenter/PP-TinyPose/fastdeploy_cn.md b/modelcenter/PP-TinyPose/fastdeploy_cn.md
new file mode 100644
index 0000000000000000000000000000000000000000..bcd653bd8333378807e3ac10110737fbdf5072fa
--- /dev/null
+++ b/modelcenter/PP-TinyPose/fastdeploy_cn.md
@@ -0,0 +1,34 @@
+## 0. 全场景高性能AI推理部署工具 FastDeploy
+FastDeploy 是一款**全场景、易用灵活、极致高效**的AI推理部署工具。提供开箱即用的**云边端**部署体验, 支持超过 150+ Text, Vision, Speech和跨模态模型，实现了AI模型**端到端的优化加速**。目前支持的硬件包括 **X86 CPU、NVIDIA GPU、ARM CPU、XPU、NPU、IPU**等10类云边端的硬件，通过一行代码切换不同推理后端和硬件。
+
+使用 FastDeploy 3步即可搞定AI模型部署：（1）安装FastDeploy预编译包（2）调用FastDeploy的API实现部署代码 （3）推理部署。
+
+**注** : 本文档下载 FastDeploy 示例来完成高性能部署体验；仅展示X86 CPU、NVIDIA GPU的推理，且默认已经准备好GPU环境（如 CUDA >= 11.2等），如需要部署其他硬件或者完整了解 FastDeploy 部署能力，请参考 [FastDeploy的GitHub仓库](https://github.com/PaddlePaddle/FastDeploy)
+
+
+## 1. 安装FastDeploy预编译包
+```
+pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
+```
+## 2. 运行部署示例
+```
+#下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/keypointdetection/tiny_pose/python
+
+# 下载PP-TinyPose模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_TinyPose_256x192_infer.tgz
+tar -xvf PP_TinyPose_256x192_infer.tgz
+wget https://bj.bcebos.com/paddlehub/fastdeploy/hrnet_demo.jpg
+
+# CPU推理
+python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.jpg --device cpu
+# GPU推理
+python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.jpg --device gpu
+# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
+python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.jpg --device gpu --use_trt True
+```
+运行完成可视化结果如下图所示:
+
+
+
+
+
+
+
+
+
+
+
+
+