Update README_cn (#142)

* Update README.md

* update

Author: shanjiang7

README.md

@@ -1,14 +1,16 @@
 # GraphNet  ![](https://img.shields.io/badge/version-v0.1-brightgreen) ![](https://img.shields.io/github/issues/PaddlePaddle/GraphNet?label=open%20issues)    [![](https://img.shields.io/badge/Contribute%20to%20GraphNet-blue)](https://github.com/PaddlePaddle/GraphNet/issues/98)
 
 
-**GraphNet** is a large-scale dataset of deep learning **computation graphs**, designed to serve as a standard benchmark and training corpus for **AI-driven tensor compiler optimization**. It contains massive, diverse graphs extracted from state-of-the-art models, enabling consistent comparison of optimization effectiveness across compiler passes, frameworks, and hardware platforms. 
+**GraphNet** is a large-scale dataset of deep learning **computation graphs**, designed to serve as a standard benchmark and training corpus for **AI-driven tensor compiler optimization**. It contains diverse graphs extracted from state-of-the-art models, enabling effective evaluation of compiler pass optimizations across frameworks and hardware platforms.
+
 
 With GraphNet, users can:
 1. Quickly benchmark the optimization performance of various compiler strategies.
 2. Easily conduct regression tests on existing compilers.
 3. Train AI‑for‑Systems models to automatically generate compiler optimization passes.
 
-**Vision**: We aim to enable cross-hardware portability of compiler optimizations by allowing models to learn and transfer optimization strategies. It will greatly reduce the manual effort required to develop efficient operator implementations.
+**Vision**: We aim to achieve cross-hardware portability of compiler optimizations by allowing models to learn and transfer optimization strategies. It will significantly  reduce the manual effort required to develop efficient operator implementations.
+
 
 
 
@@ -24,7 +26,7 @@ With GraphNet, users can:
 
 
 ## ⚡ Quick Start
-
+For full implementation details, please refer to the [Co-Creation Tutorial](https://github.com/PaddlePaddle/GraphNet/blob/develop/CONTRIBUTE_TUTORIAL.md#co-creation-tutorial).
 ### Benchmark your compiler on the model:
 
 **graph_net.torch.test_compiler** 
@@ -89,7 +91,8 @@ python -m graph_net.config --global \
   --username "your-name" \
   --email "your-email"
 ```
-Once you have packaged these extracted computation graphs, submit them to the GraphNet community via the following group chats.
+Once you have packaged these extracted computation graphs, submit them to the GraphNet community via the following group chats. [Discord](https://discord.gg/Pp5FKW92) is also available.
+
 
 <div align="center">
 <table>
@@ -104,6 +107,7 @@ Once you have packaged these extracted computation graphs, submit them to the Gr
 </table>
 </div>
 
+
 ##  License
 This project is released under the [MIT License](LICENSE).
 

README_cn.md

@@ -1,13 +1,15 @@
-# GraphNet
+# GraphNet ![](https://img.shields.io/badge/version-v0.1-brightgreen) ![](https://img.shields.io/github/issues/PaddlePaddle/GraphNet?label=open%20issues)    [![](https://img.shields.io/badge/开源活动-blue)](https://github.com/PaddlePaddle/GraphNet/issues/98)
 
-GraphNet —— 一个面向编译器开发的大规模数据集，旨在为研究者提供一个统一、开放的实验平台。其中包含大量来自真实模型的计算图，方便评估不同编译器Pass的优化效果。
+**GraphNet** 是一个面向AI 编译器开发的大规模**计算图数据集**，旨在为研究者提供统一、开放的实验平台。它收录了大量深度学习模型的计算图，方便评估不同编译器 Pass 的优化效果，支持跨框架、跨平台的性能比较。
 
 通过 GraphNet，用户可以：
 
 1. 快速测试不同编译器策略的通用优化效果
 2. 方便已有编译器做回归测试
 3. 训练AI-for-system模型以自动生成编译器优化Pass
 
+**目标**：我们致力于实现编译优化策略在不同硬件间的可移植性，使大模型能够学习并迁移这些策略，从而大幅降低高效算子开发的成本。
+
 ### 数据集构建约束：
 
 1. 动态图能正常运行
@@ -19,7 +21,10 @@ GraphNet —— 一个面向编译器开发的大规模数据集，旨在为研
 7. 若存在自定义算子，则自定义算子的代码必须能被完整访问
 8. 可通过统一方式配置计算图在不同芯片上运行
 
+
+
 ## 快速开始
+详细的实现细节请参见 [共创者指引](https://github.com/PaddlePaddle/GraphNet/blob/develop/CONTRIBUTE_TUTORIAL_cn.md#共创者指引)
 ### 测试编译器性能
 **graph_net.torch.test_compiler** 
 ```