Remove alpha reference and update project status

LiteRT-PiperOrigin-RevId: 846879237

Author: ai-edge-bot

README.md

@@ -16,22 +16,12 @@ platforms, via efficient conversion, runtime, and optimization
 LiteRT continues the legacy of TensorFlow Lite as the trusted, high-performance
 runtime for on-device AI.
 
-LiteRT V1 supports the TensorFlow Lite APIs, and is the recommended
-solution for existing apps using those older APIs.
-
-LiteRT V2 (aka Next as announced at Google IO '25), introduces a new set of
-APIs, featuring advanced GPU/NPU acceleration, delivering superior performance,
-and making on-device ML inference easier than ever.
-
-### 🚀 Status: General Availability (for V1) / Alpha (for V2)
-
-- LiteRT V1 supports the TensorFlow Lite API, and its release status is
-  [general availability](https://support.google.com/a/answer/11202276?hl=en).
-- LiteRT V2 is an alpha release and under active development.
+LiteRT features advanced GPU/NPU acceleration, delivers superior ML & GenAI
+performance, making on-device ML inference easier than ever.
 
 ### 🌟 What's New
 
-- **🆕 New LiteRT v2 API**: Streamline development with automated accelerator
+- **🆕 New LiteRT Compiled Model API**: Streamline development with automated accelerator
   selection, true async execution, and efficient I/O buffer handling.
 
   - Automated accelerator selection vs explicit delegate creation
@@ -157,17 +147,6 @@ deployment. Our product strategies are:
 - **Platform Support**: Enhancing support for core platforms and exploring new
   ones.
 
-## 🗺 What's Next:
-
-**V2 Beta by Dec 2025:**
-
-- Achieve feature parity with TensorFlow Lite
-- Expand GPU backend support
-- Proactively increase ML and GenAI model coverage
-- Broader LiteRT Runtime/Converter upgrades from TensorFlow Lite
-
-**V2 General Availability by Google IO, May 2026**
-
 ## 🙌 Contributing
 
 We welcome contributions to LiteRT. Please see the
@@ -204,7 +183,6 @@ Check out these other projects from Google:
 - **[XNNPACK](https://github.com/google/XNNPACK)**: A highly optimized library
   of neural network inference operators for ARM, x86, and WebAssembly
   architectures that provides high-performance CPU acceleration for LiteRT.
-- **V2 GPU Delegate** - Coming soon
 - **[MediaPipe](https://github.com/google-ai-edge/mediapipe)**: A framework for
   building cross-platform, customizable ML solutions for live and streaming
   media.

RELEASE.md

@@ -1,3 +1,80 @@
+# Release 2.1.0
+
+**Release 2.1.0 is the LiteRT** beta release.
+
+LiteRT APIs are stable and have achieved feature parity. This milestone marks a significant step forward, introducing full feature parity with TensorFlow Lite, stable LiteRT APIs, and critical performance enhancements for GPU and NPU acceleration. With this release, we are officially recommending that developers begin their transition to LiteRT.
+
+## Major Features and Improvements
+
+### LiteRT Runtime
+
+* Custom op is supported through [custom op dispatcher](https://github.com/google-ai-edge/LiteRT/blob/main/g3doc/apis/Custom_Op_Dispatcher.md).
+* CMake Build is supported in addition to Bazel
+* Released LiteRT C++ SDK using prebuilt libLiteRt.so file
+* Added Profiler API in CompiledModel
+* Added ErrorReporter API in CompiledModel
+* Added ResizeInputTensor API in CompiledModel
+
+### LiteRT NPU
+
+* Introduced LiteRT Accelerator Test Suite for coverage and regression testing
+* Introduced LiteRT graph transformation APIs for compiler plugins
+* Qualcomm
+  * Added support for Qualcomm Snapdragon Gen5
+  * Added support for NPU JIT mode
+  * LiteRT Op coverage improvements
+* MediaTek
+  * Added support for NPU JIT mode
+  * LiteRT Op coverage improvements
+
+### LiteRT GPU
+
+* Increased GPU coverage with WebGPU/Dawn and OpenCL including Android, Linux, MacOS, Windows, iOS, IoT devices
+* Added asynchronous execution to Metal, WebGPU backends
+* Improved performance and memory footprint
+* Added an option to control GPU inference priority
+* Better error handling (without crashing) on Delegation errors
+
+### LLM Support
+
+* Provided Desktop GPU backends prebuilt for Linux (x64, arm64), MacOS (arm64), Windows (x64)
+* Improved memory utilization when executing on GPUs
+* Published new LLMs on [https://huggingface.co/litert-community](https://huggingface.co/litert-community)
+  * litert-community/FastVLM-0.5B
+  * litert-community/Qwen3-0.6B
+  * litert-community/embeddinggemma-300m with new NPU precompiled models
+  * litert-community/gemma-3-270m-it with new NPU precompiled model
+* Published Function Gemma on [https://huggingface.co/google](https://huggingface.co/google)
+  * google/functiongemma-270m-it
+
+### LiteRT on Android
+
+* Added Interpreter API (CPU only) in the Maven v2.1.0+ packages
+* Added [Instruction](https://ai.google.dev/edge/litert/next/android_cpp_sdk) to use pre-built CompiledModell C++ API from the Maven package.
+
+## Bug Fixes and Other Changes
+
+Fixes Android min SDK version and it’s 23 now.
+
+LiteRT NPU: Fixes partition algorithm when the full model cannot be offloaded to NPU.
+
+## Breaking Changes
+
+* Removed direct C headers usage. Users no longer need to include C headers.
+* TensorBuffer::CreateManaged() requires Environment always.
+* All TensorBuffer creation requires Environment except HostMemory types.
+* LiteRT C++ constructors are hidden. All LiteRT C++ objects should be created by Create() methods.
+* Moved internal only C++ APIs(such as litert\_logging.h) to litert/cc/internal
+* Removed Tensor, Subgraph, Signature access from litert::Model. Instead users can access SimpleTensor, SimpleSignature.
+* The CompiledModel::Create() API no longer needs litert::Model. They can be created from filename, model buffers directly.
+* Users can access SimpleTensor and SimpleSignature from CompiledModel.
+* Annotation, Metrics APIs are removed from CompiledModel.
+* Removed individual OpaqueOptions creation. These OpaqueOptions objects are obtained by Options directly.
+  *  Options::GetCpuOptions()
+  *  Options::GetGpuOptions()
+  *  Options::GetRuntimeOptions()
+  * …
+
 # Release 2.0.2a1
 
 ## LiteRT