From 7de1a4560520477a538fb7636626ea006ae2707c Mon Sep 17 00:00:00 2001
From: wehub-resource-sync <resource-sync@wehub.local>
Date: Mon, 13 Jul 2026 10:45:11 +0000
Subject: [PATCH] docs: preserve upstream English README

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+<!--- 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 -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- 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
+==============================================
+[Documentation](https://tvm.apache.org/docs) |
+[Contributors](CONTRIBUTORS.md) |
+[Community](https://tvm.apache.org/community) |
+[Release Notes](NEWS.md)
+
+Apache TVM is an open machine learning compilation framework,
+following the following principles:
+
+- Python-first development that enables quick customization of machine learning compiler pipelines.
+- Universal deployment to bring models into minimum deployable modules.
+
+License
+-------
+TVM is licensed under the [Apache-2.0](LICENSE) license.
+
+Getting Started
+---------------
+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.
+
+Contribute to TVM
+-----------------
+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/).
+
+History and Acknowledgement
+---------------------------
+TVM started as a research project for deep learning compilation.
+The first version of the project benefited a lot from the following projects:
+
+- [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.
+
+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.
+
+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.