docs: make Chinese README the default

README.md

@@ -1,3 +1,9 @@
+<!-- WEHUB_ZH_README -->
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
+> [English](./README.en.md) · [原始项目](https://github.com/apache/tvm) · [上游 README](https://github.com/apache/tvm/blob/HEAD/README.md)  
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
+
 <!--- Licensed to the Apache Software Foundation (ASF) under one -->
 <!--- or more contributor license agreements.  See the NOTICE file -->
 <!--- distributed with this work for additional information -->
@@ -15,51 +21,50 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
-<img src=https://raw.githubusercontent.com/apache/tvm-site/main/images/logo/tvm-logo-small.png width=128/> Open Machine Learning Compiler Framework
+<img src=https://raw.githubusercontent.com/apache/tvm-site/main/images/logo/tvm-logo-small.png width=128/> 开放机器学习编译器框架
 ==============================================
-[Documentation](https://tvm.apache.org/docs) |
-[Contributors](CONTRIBUTORS.md) |
-[Community](https://tvm.apache.org/community) |
-[Release Notes](NEWS.md)
+[文档](https://tvm.apache.org/docs) |
+[贡献者](CONTRIBUTORS.md) |
+[社区](https://tvm.apache.org/community) |
+[发布说明](NEWS.md)
 
-Apache TVM is an open machine learning compilation framework,
-following the following principles:
+Apache TVM 是一个开放的机器学习编译框架，
+遵循以下原则：
 
-- Python-first development that enables quick customization of machine learning compiler pipelines.
-- Universal deployment to bring models into minimum deployable modules.
+- 以 Python 为先（Python-first）的开发方式，可快速定制机器学习编译器流水线。
+- 通用部署（Universal deployment），将模型交付为最小可部署模块。
 
-License
+许可证
 -------
-TVM is licensed under the [Apache-2.0](LICENSE) license.
+TVM 根据 [Apache-2.0](LICENSE) 许可证授权。
 
-Getting Started
+入门
 ---------------
-Check out the [TVM Documentation](https://tvm.apache.org/docs/) site for installation instructions, tutorials, examples, and more.
-The [Getting Started with TVM](https://tvm.apache.org/docs/get_started/overview.html) tutorial is a great
-place to start.
+请访问 [TVM 文档](https://tvm.apache.org/docs/) 网站，获取安装说明、教程、示例等更多信息。
+[TVM 入门](https://tvm.apache.org/docs/get_started/overview.html) 教程是很好的
+起点。
 
-Contribute to TVM
+为 TVM 做贡献
 -----------------
-TVM adopts the Apache committer model. We aim to create an open-source project maintained and owned by the community.
-Check out the [Contributor Guide](https://tvm.apache.org/docs/contribute/).
+TVM 采用 Apache 提交者（committer）模式。我们的目标是创建一个由社区维护和拥有的开源项目。
+请参阅 [贡献者指南](https://tvm.apache.org/docs/contribute/).
 
-History and Acknowledgement
+历史与致谢
 ---------------------------
-TVM started as a research project for deep learning compilation.
-The first version of the project benefited a lot from the following projects:
+TVM 最初是作为深度学习编译的研究项目启动的。
+该项目的第一版从以下项目中受益匪浅：
 
-- [Halide](https://github.com/halide/Halide): Part of TVM's TIR and arithmetic simplification module
- originates from Halide. We also learned and adapted some parts of the lowering pipeline from Halide.
-- [Loopy](https://github.com/inducer/loopy): use of integer set analysis and its loop transformation primitives.
-- [Theano](https://github.com/Theano/Theano): the design inspiration of symbolic scan operator for recurrence.
+- [Halide](https://github.com/halide/Halide): TVM 的 TIR 与算术化简模块的部分
+ 源自 Halide。我们也借鉴并改编了 Halide 降级流水线的部分内容。
+- [Loopy](https://github.com/inducer/loopy): 对整数集分析及其循环变换原语的使用。
+- [Theano](https://github.com/Theano/Theano): 循环符号扫描算子（symbolic scan operator）的设计灵感。
 
-Since then, the project has gone through several rounds of redesigns.
-The current design is also drastically different from the initial design, following the
-development trend of the ML compiler community.
+此后，该项目经历了多轮重新设计。
+当前设计与初始设计也截然不同，顺应了 ML 编译器社区的发展趋势。
 
-The most recent version focuses on a cross-level design with TensorIR as the tensor-level representation
-and Relax as the graph-level representation and Python-first transformations.
-The project's current design goal is to make the ML compiler accessible by enabling most
-transformations to be customizable in Python and bringing a cross-level representation that can jointly
-optimize computational graphs, tensor programs, and libraries. The project is also a foundation
-infra for building Python-first vertical compilers for domains, such as LLMs.
+最新版本聚焦于跨层级设计：以 TensorIR 作为张量级表示，
+以 Relax 作为图级表示，并支持 Python 优先的变换。
+项目当前的设计目标是，通过让大多数变换可在 Python 中定制，
+并提供可联合优化计算图、张量程序和库的跨层级表示，使 ML 编译器更易用。
+该项目也是为特定领域构建 Python 优先垂直编译器的基础
+基础设施，例如 LLM。