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# InternLM 1.8B 安卓端部署实践
![image](https://github.com/user-attachments/assets/5ede99d5-e82b-4ff8-acd8-407f9277967a)

本文将带大家手把手使用[mlc-llm](https://llm.mlc.ai/docs/deploy/android.html#android-sdk)将 InternLM2 部署到安卓手机上

首先我们来看一下最终的效果～
<div align="center">
<img src="https://github.com/user-attachments/assets/cd9fe502-490e-40c6-8649-30671a4fe504" width="50%" height="50%">
</div>

## 1 环境准备
### 1.1 安装rust
参考 [https://forge.rust-lang.org/infra/other-installation-methods.html#which](https://forge.rust-lang.org/infra/other-installation-methods.html#which)

使用了国内的镜像，出现选项直接Enter

```
export RUSTUP_DIST_SERVER=https://mirrors.ustc.edu.cn/rust-static
export RUSTUP_UPDATE_ROOT=https://mirrors.ustc.edu.cn/rust-static/rustup
curl --proto '=https' --tlsv1.2 -sSf https://mirrors.ustc.edu.cn/misc/rustup-install.sh | sh
```

### 1.2 安装Android Studio
参考 [https://developer.android.com/studio](https://developer.android.com/studio)

```
mkdir -p /root/android && cd /root/android
wget https://redirector.gvt1.com/edgedl/android/studio/ide-zips/2024.1.1.12/android-studio-2024.1.1.12-linux.tar.gz
tar -xvzf android-studio-2024.1.1.12-linux.tar.gz
cd android-studio
wget https://dl.google.com/android/repository/commandlinetools-linux-11076708_latest.zip?hl=zh-cn
unzip commandlinetools-linux-11076708_latest.zip\?hl\=zh-cn
export JAVA_HOME=/root/Downloads/android-studio/jbr
cmdline-tools/bin/sdkmanager "ndk;27.0.12077973" "cmake;3.22.1" "platforms;android-34" "build-tools;33.0.1" --sdk_root='sdk'
```


### 1.3 设置环境变量
```
. "$HOME/.cargo/env"
export ANDROID_NDK=/root/android/android-studio/sdk/ndk/27.0.12077973
export TVM_NDK_CC=$ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64/bin/aarch64-linux-android24-clang
export JAVA_HOME=/root/android//android-studio/jbr
export ANDROID_HOME=/root/android/android-studio/sdk
export PATH=/usr/local/cuda-12/bin:$PATH
export PATH=/root/android/android-studio/sdk/cmake/3.22.1/bin:$PATH
```
## 2 转换模型
### 2.1 安装mlc-llm
参考[https://llm.mlc.ai/docs/install/mlc_llm.html](https://llm.mlc.ai/docs/install/mlc_llm.html)
（如果下载很慢可以取消重新运行一下，或者本地下载了拷过去）
```
conda create --name mlc-prebuilt python=3.11
conda activate mlc-prebuilt
conda install -c conda-forge git-lfs
pip install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 transformers sentencepiece protobuf
wget https://github.com/mlc-ai/package/releases/download/v0.9.dev0/mlc_llm_nightly_cu122-0.1.dev1445-cp311-cp311-manylinux_2_28_x86_64.whl
wget https://github.com/mlc-ai/package/releases/download/v0.9.dev0/mlc_ai_nightly_cu122-0.15.dev404-cp311-cp311-manylinux_2_28_x86_64.whl
pip install mlc_ai_nightly_cu122-0.15.dev404-cp311-cp311-manylinux_2_28_x86_64.whl
pip install mlc_llm_nightly_cu122-0.1.dev1445-cp311-cp311-manylinux_2_28_x86_64.whl
```
测试如下输出说明安装正确

```
python -c "import mlc_llm; print(mlc_llm)"
```
![image](https://github.com/user-attachments/assets/f497f704-de19-4043-b901-af26856a62c9)

克隆项目
```
git clone https://github.com/mlc-ai/mlc-llm.git
cd mlc-llm
git submodule update --init --recursive
```

### 2.2 转换参数
You can be under the mlc-llm repo, or your own working directory. Note that all platforms can share the same compiled/quantized weights. See [Compile Command Specification](https://llm.mlc.ai/docs/compilation/compile_models.html#compile-command-specification) for specification of `convert_weight`.

```
cd android/MLCChat
export TVM_SOURCE_DIR=/root/android/mlc-llm/3rdparty/tvm
export MLC_LLM_SOURCE_DIR=/root/android/mlc-llm
mlc_llm convert_weight /root/models/internlm2-chat-1_8b-sft/ \
--quantization q4f16_1 \
-o dist/internlm2-chat-1_8b-sft-q4f16_1-MLC
```
### 2.3 生成配置
Use mlc_llm gen_config to generate mlc-chat-config.json and process tokenizers. See [Compile Command Specification](https://llm.mlc.ai/docs/compilation/compile_models.html#compile-command-specification) for specification of `gen_config`.

```

mlc_llm gen_config /root/models/internlm2-chat-1_8b-sft/ \
--quantization q4f16_1 --conv-template chatml \
-o dist/internlm2-chat-1_8b-sft-q4f16_1-MLC

```
### 2.4 上传到huggingface
上传这一步需要能访问huggingface，可能需要部署代理
如果没有代理可以直接在接下来的配置中使用如下链接的模型（和文档中的转换方法一样）
[https://huggingface.co/timws/internlm2-chat-1_8b-sft-q4f16_1-MLC](https://huggingface.co/timws/internlm2-chat-1_8b-sft-q4f16_1-MLC)
### 2.5 (可选) 测试转换的模型
在打包之前可以测试模型效果，需要编译成二进制文件
在个人电脑上运行测试代码正常，**InternStudio**上**暂未成功**
```

mlc_llm compile ./dist/internlm2-chat-1_8b-sft-q4f16_1-MLC/mlc-chat-config.json \
--device cuda -o dist/libs/internlm2-chat-1_8b-sft-q4f16_1-MLC-cuda.so
```
测试编译的模型是否符合预期，手机端运行的效果和测试效果接近
```python3
from mlc_llm import MLCEngine

# Create engine
engine = MLCEngine(model="./dist/internlm2-1_8b-q4f16_1-MLC", model_lib="./dist/libs/internlm2-1_8b-q4f16_1-MLC-cuda.so")

# Run chat completion in OpenAI API.
print(engine)
for response in engine.chat.completions.create(
messages=[{"role": "user", "content": "你是谁？"}],
stream=True
):
for choice in response.choices:
print(choice.delta.content, end="", flush=True)
print("\n")
engine.terminate()
```

## 3 打包运行
### 3.1 修改配置文件
修改`mlc-package-config.json`
参考如下
```
{
"device": "android",
"model_list": [
{
"model": "HF://timws/internlm2-chat-1_8b-sft-q4f16_1-MLC",
"estimated_vram_bytes": 3980990464,
"model_id": "internlm2-chat-1_8b-sft-q4f16_1-MLC"

},
{
"model": "HF://mlc-ai/gemma-2b-it-q4f16_1-MLC",
"model_id": "gemma-2b-q4f16_1-MLC",
"estimated_vram_bytes": 3980990464
}
]
}

```

### 3.2 运行打包命令

```
mlc_llm package
```
![image](https://github.com/user-attachments/assets/0e1db9c5-6252-4c6f-81b2-a739fa4fac44)


### 3.3 创建签名
```
cd /root/android/mlc-llm/android/MLCChat
/root/android/android-studio/jbr/bin/keytool -genkey -v -keystore my-release-key.jks -keyalg RSA -keysize 2048 -validity 10000
Enter keystore password:
Re-enter new password:
What is your first and last name?
[Unknown]: Any
What is the name of your organizational unit?
[Unknown]: Any
What is the name of your organization?
[Unknown]: Any
What is the name of your City or Locality?
[Unknown]: Any
What is the name of your State or Province?
[Unknown]: Any
What is the two-letter country code for this unit?
[Unknown]: CN
Is CN=Any, OU=Any, O=Any, L=Any, ST=Any, C=CN correct?
[no]: yes

Generating 2,048 bit RSA key pair and self-signed certificate (SHA256withRSA) with a validity of 10,000 days
for: CN=Any, OU=Any, O=Any, L=Any, ST=Any, C=CN
[Storing my-release-key.jks]
```
### 3.4 修改gradle配置
如果是本地可以WIFI或USB调试不用签名，在服务器构建需要签名
修改`app/build.gradle`为如下内容，主要是增加了签名部分，注意确认签名文件的位置
```
plugins {
id 'com.android.application'
id 'org.jetbrains.kotlin.android'
}

android {
namespace 'ai.mlc.mlcchat'
compileSdk 34

defaultConfig {
applicationId "ai.mlc.mlcchat"
minSdk 26
targetSdk 33
versionCode 1
versionName "1.0"

testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
vectorDrawables {
useSupportLibrary true
}
}


compileOptions {
sourceCompatibility JavaVersion.VERSION_1_8
targetCompatibility JavaVersion.VERSION_1_8
}
kotlinOptions {
jvmTarget = '1.8'
}
buildFeatures {
compose true
}
composeOptions {
kotlinCompilerExtensionVersion '1.4.3'
}
packagingOptions {
resources {
excludes += '/META-INF/{AL2.0,LGPL2.1}'
}
}

signingConfigs {
release {
storeFile file("/root/android/mlc-llm/android/MLCChat/my-release-key.jks")
storePassword "123456"
keyAlias "mykey"
keyPassword "123456"
}
}

buildTypes {
release {
minifyEnabled false
proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
signingConfig signingConfigs.release
}
}
}

dependencies {
implementation project(":mlc4j")
implementation 'androidx.core:core-ktx:1.10.1'
implementation 'androidx.lifecycle:lifecycle-runtime-ktx:2.6.1'
implementation 'androidx.activity:activity-compose:1.7.1'
implementation platform('androidx.compose:compose-bom:2022.10.00')
implementation 'androidx.lifecycle:lifecycle-viewmodel-compose:2.6.1'
implementation 'androidx.compose.ui:ui'
implementation 'androidx.compose.ui:ui-graphics'
implementation 'androidx.compose.ui:ui-tooling-preview'
implementation 'androidx.compose.material3:material3:1.1.0'
implementation 'androidx.compose.material:material-icons-extended'
implementation 'androidx.appcompat:appcompat:1.6.1'
implementation 'androidx.navigation:navigation-compose:2.5.3'
implementation 'com.google.code.gson:gson:2.10.1'
implementation fileTree(dir: 'src/main/libs', include: ['*.aar', '*.jar'], exclude: [])
testImplementation 'junit:junit:4.13.2'
androidTestImplementation 'androidx.test.ext:junit:1.1.5'
androidTestImplementation 'androidx.test.espresso:espresso-core:3.5.1'
androidTestImplementation platform('androidx.compose:compose-bom:2022.10.00')
androidTestImplementation 'androidx.compose.ui:ui-test-junit4'
debugImplementation 'androidx.compose.ui:ui-tooling'
debugImplementation 'androidx.compose.ui:ui-test-manifest'

}
```

### 3.5 命令行编译
运行编译命令，完成后在`app/build/outputs/apk/release`生成`app-release.apk`安装包，下载到手机上运行
运行App需要能访问huggingface下载模型(参考文档中的bundle方法需要ADB刷入模型数据)
```
./gradlew assembleRelease
```
![image](https://github.com/user-attachments/assets/df7673f7-7128-4079-b77d-37093fff6660)



### 3.6 运行体验
运行App需要能访问huggingface下载模型

<div align="center">
<img src="https://github.com/user-attachments/assets/cd9fe502-490e-40c6-8649-30671a4fe504" width="50%" height="50%">
</div>

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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 Merged

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Aug 2, 2024
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add tutorial for android
#1448
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qew21 Aug 1, 2024
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# InternLM 1.8B 安卓端部署实践
![image](https://github.com/user-attachments/assets/5ede99d5-e82b-4ff8-acd8-407f9277967a)

本文将带大家手把手使用[mlc-llm](https://llm.mlc.ai/docs/deploy/android.html#android-sdk)将 InternLM2 部署到安卓手机上

首先我们来看一下最终的效果～
<div align="center">
 <img src="https://github.com/user-attachments/assets/cd9fe502-490e-40c6-8649-30671a4fe504" width="50%" height="50%">
</div>

## 1 环境准备
### 1.1 安装rust 
参考 [https://forge.rust-lang.org/infra/other-installation-methods.html#which](https://forge.rust-lang.org/infra/other-installation-methods.html#which)

使用了国内的镜像，出现选项直接Enter

```
export RUSTUP_DIST_SERVER=https://mirrors.ustc.edu.cn/rust-static
export RUSTUP_UPDATE_ROOT=https://mirrors.ustc.edu.cn/rust-static/rustup
curl --proto '=https' --tlsv1.2 -sSf https://mirrors.ustc.edu.cn/misc/rustup-install.sh | sh
```

### 1.2 安装Android Studio
参考 [https://developer.android.com/studio](https://developer.android.com/studio)

```
mkdir -p /root/android && cd /root/android
wget https://redirector.gvt1.com/edgedl/android/studio/ide-zips/2024.1.1.12/android-studio-2024.1.1.12-linux.tar.gz
tar -xvzf android-studio-2024.1.1.12-linux.tar.gz
cd android-studio
wget https://dl.google.com/android/repository/commandlinetools-linux-11076708_latest.zip?hl=zh-cn
unzip commandlinetools-linux-11076708_latest.zip\?hl\=zh-cn
export JAVA_HOME=/root/Downloads/android-studio/jbr
cmdline-tools/bin/sdkmanager "ndk;27.0.12077973" "cmake;3.22.1" "platforms;android-34" "build-tools;33.0.1" --sdk_root='sdk'
```


### 1.3 设置环境变量
```
. "$HOME/.cargo/env"
export ANDROID_NDK=/root/android/android-studio/sdk/ndk/27.0.12077973
export TVM_NDK_CC=$ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64/bin/aarch64-linux-android24-clang
export JAVA_HOME=/root/android//android-studio/jbr
export ANDROID_HOME=/root/android/android-studio/sdk
export PATH=/usr/local/cuda-12/bin:$PATH
export PATH=/root/android/android-studio/sdk/cmake/3.22.1/bin:$PATH
```
## 2 转换模型
### 2.1 安装mlc-llm
参考[https://llm.mlc.ai/docs/install/mlc_llm.html](https://llm.mlc.ai/docs/install/mlc_llm.html)
（如果下载很慢可以取消重新运行一下，或者本地下载了拷过去）
```
conda create --name mlc-prebuilt python=3.11
conda activate mlc-prebuilt
conda install -c conda-forge git-lfs
pip install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 transformers sentencepiece protobuf
wget https://github.com/mlc-ai/package/releases/download/v0.9.dev0/mlc_llm_nightly_cu122-0.1.dev1445-cp311-cp311-manylinux_2_28_x86_64.whl
wget https://github.com/mlc-ai/package/releases/download/v0.9.dev0/mlc_ai_nightly_cu122-0.15.dev404-cp311-cp311-manylinux_2_28_x86_64.whl
pip install mlc_ai_nightly_cu122-0.15.dev404-cp311-cp311-manylinux_2_28_x86_64.whl
pip install mlc_llm_nightly_cu122-0.1.dev1445-cp311-cp311-manylinux_2_28_x86_64.whl
```
测试如下输出说明安装正确

```
python -c "import mlc_llm; print(mlc_llm)"
```
![image](https://github.com/user-attachments/assets/f497f704-de19-4043-b901-af26856a62c9)

克隆项目
```
git clone https://github.com/mlc-ai/mlc-llm.git
cd mlc-llm
git submodule update --init --recursive
```

### 2.2 转换参数
You can be under the mlc-llm repo, or your own working directory. Note that all platforms can share the same compiled/quantized weights. See [Compile Command Specification](https://llm.mlc.ai/docs/compilation/compile_models.html#compile-command-specification) for specification of `convert_weight`.

```
cd android/MLCChat 
export TVM_SOURCE_DIR=/root/android/mlc-llm/3rdparty/tvm
export MLC_LLM_SOURCE_DIR=/root/android/mlc-llm
mlc_llm convert_weight /root/models/internlm2-chat-1_8b-sft/ \
 --quantization q4f16_1 \
 -o dist/internlm2-chat-1_8b-sft-q4f16_1-MLC
```
### 2.3 生成配置
Use mlc_llm gen_config to generate mlc-chat-config.json and process tokenizers. See [Compile Command Specification](https://llm.mlc.ai/docs/compilation/compile_models.html#compile-command-specification) for specification of `gen_config`.

```

mlc_llm gen_config /root/models/internlm2-chat-1_8b-sft/ \
 --quantization q4f16_1 --conv-template chatml \
 -o dist/internlm2-chat-1_8b-sft-q4f16_1-MLC

```
### 2.4 上传到huggingface
上传这一步需要能访问huggingface，可能需要部署代理
如果没有代理可以直接在接下来的配置中使用如下链接的模型（和文档中的转换方法一样）
[https://huggingface.co/timws/internlm2-chat-1_8b-sft-q4f16_1-MLC](https://huggingface.co/timws/internlm2-chat-1_8b-sft-q4f16_1-MLC)
### 2.5 (可选) 测试转换的模型
在打包之前可以测试模型效果，需要编译成二进制文件
在个人电脑上运行测试代码正常，**InternStudio**上**暂未成功**
```

mlc_llm compile ./dist/internlm2-chat-1_8b-sft-q4f16_1-MLC/mlc-chat-config.json \
 --device cuda -o dist/libs/internlm2-chat-1_8b-sft-q4f16_1-MLC-cuda.so
```
测试编译的模型是否符合预期，手机端运行的效果和测试效果接近
```python3
from mlc_llm import MLCEngine

# Create engine
engine = MLCEngine(model="./dist/internlm2-1_8b-q4f16_1-MLC", model_lib="./dist/libs/internlm2-1_8b-q4f16_1-MLC-cuda.so")

# Run chat completion in OpenAI API.
print(engine)
for response in engine.chat.completions.create(
 messages=[{"role": "user", "content": "你是谁？"}],
 stream=True
):
 for choice in response.choices:
 print(choice.delta.content, end="", flush=True)
print("\n")
engine.terminate()
```

## 3 打包运行
### 3.1 修改配置文件
修改`mlc-package-config.json`
参考如下
```
{
 "device": "android",
 "model_list": [
 {
 "model": "HF://timws/internlm2-chat-1_8b-sft-q4f16_1-MLC",
 "estimated_vram_bytes": 3980990464,
 "model_id": "internlm2-chat-1_8b-sft-q4f16_1-MLC"

 },
 {
 "model": "HF://mlc-ai/gemma-2b-it-q4f16_1-MLC",
 "model_id": "gemma-2b-q4f16_1-MLC",
 "estimated_vram_bytes": 3980990464
 }
 ]
}

```

### 3.2 运行打包命令

```
 mlc_llm package
```
![image](https://github.com/user-attachments/assets/0e1db9c5-6252-4c6f-81b2-a739fa4fac44)


### 3.3 创建签名
```
cd /root/android/mlc-llm/android/MLCChat
/root/android/android-studio/jbr/bin/keytool -genkey -v -keystore my-release-key.jks -keyalg RSA -keysize 2048 -validity 10000
Enter keystore password: 
Re-enter new password: 
What is your first and last name?
 [Unknown]: Any
What is the name of your organizational unit?
 [Unknown]: Any
What is the name of your organization?
 [Unknown]: Any
What is the name of your City or Locality?
 [Unknown]: Any
What is the name of your State or Province?
 [Unknown]: Any
What is the two-letter country code for this unit?
 [Unknown]: CN
Is CN=Any, OU=Any, O=Any, L=Any, ST=Any, C=CN correct?
 [no]: yes

Generating 2,048 bit RSA key pair and self-signed certificate (SHA256withRSA) with a validity of 10,000 days
 for: CN=Any, OU=Any, O=Any, L=Any, ST=Any, C=CN
[Storing my-release-key.jks]
```
### 3.4 修改gradle配置
如果是本地可以WIFI或USB调试不用签名，在服务器构建需要签名
修改`app/build.gradle`为如下内容，主要是增加了签名部分，注意确认签名文件的位置
```
plugins {
 id 'com.android.application'
 id 'org.jetbrains.kotlin.android'
}

android {
 namespace 'ai.mlc.mlcchat'
 compileSdk 34

 defaultConfig {
 applicationId "ai.mlc.mlcchat"
 minSdk 26
 targetSdk 33
 versionCode 1
 versionName "1.0"

 testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
 vectorDrawables {
 useSupportLibrary true
 }
 }


 compileOptions {
 sourceCompatibility JavaVersion.VERSION_1_8
 targetCompatibility JavaVersion.VERSION_1_8
 }
 kotlinOptions {
 jvmTarget = '1.8'
 }
 buildFeatures {
 compose true
 }
 composeOptions {
 kotlinCompilerExtensionVersion '1.4.3'
 }
 packagingOptions {
 resources {
 excludes += '/META-INF/{AL2.0,LGPL2.1}'
 }
 }

 signingConfigs {
 release {
 storeFile file("/root/android/mlc-llm/android/MLCChat/my-release-key.jks")
 storePassword "123456"
 keyAlias "mykey"
 keyPassword "123456"
 }
 }

 buildTypes {
 release {
 minifyEnabled false
 proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
 signingConfig signingConfigs.release
 }
 }
}

dependencies {
 implementation project(":mlc4j")
 implementation 'androidx.core:core-ktx:1.10.1'
 implementation 'androidx.lifecycle:lifecycle-runtime-ktx:2.6.1'
 implementation 'androidx.activity:activity-compose:1.7.1'
 implementation platform('androidx.compose:compose-bom:2022.10.00')
 implementation 'androidx.lifecycle:lifecycle-viewmodel-compose:2.6.1'
 implementation 'androidx.compose.ui:ui'
 implementation 'androidx.compose.ui:ui-graphics'
 implementation 'androidx.compose.ui:ui-tooling-preview'
 implementation 'androidx.compose.material3:material3:1.1.0'
 implementation 'androidx.compose.material:material-icons-extended'
 implementation 'androidx.appcompat:appcompat:1.6.1'
 implementation 'androidx.navigation:navigation-compose:2.5.3'
 implementation 'com.google.code.gson:gson:2.10.1'
 implementation fileTree(dir: 'src/main/libs', include: ['*.aar', '*.jar'], exclude: [])
 testImplementation 'junit:junit:4.13.2'
 androidTestImplementation 'androidx.test.ext:junit:1.1.5'
 androidTestImplementation 'androidx.test.espresso:espresso-core:3.5.1'
 androidTestImplementation platform('androidx.compose:compose-bom:2022.10.00')
 androidTestImplementation 'androidx.compose.ui:ui-test-junit4'
 debugImplementation 'androidx.compose.ui:ui-tooling'
 debugImplementation 'androidx.compose.ui:ui-test-manifest'

}
```

### 3.5 命令行编译
运行编译命令，完成后在`app/build/outputs/apk/release`生成`app-release.apk`安装包，下载到手机上运行
运行App需要能访问huggingface下载模型(参考文档中的bundle方法需要ADB刷入模型数据)
```
./gradlew assembleRelease
```
![image](https://github.com/user-attachments/assets/df7673f7-7128-4079-b77d-37093fff6660)



### 3.6 运行体验
运行App需要能访问huggingface下载模型

<div align="center">
 <img src="https://github.com/user-attachments/assets/cd9fe502-490e-40c6-8649-30671a4fe504" width="50%" height="50%">
</div>


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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add tutorial for android
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# InternLM 1.8B 安卓端部署实践
![image](https://github.com/user-attachments/assets/5ede99d5-e82b-4ff8-acd8-407f9277967a)

本文将带大家手把手使用[mlc-llm](https://llm.mlc.ai/docs/deploy/android.html#android-sdk)将 InternLM2 部署到安卓手机上

首先我们来看一下最终的效果～
<div align="center">
 <img src="https://github.com/user-attachments/assets/cd9fe502-490e-40c6-8649-30671a4fe504" width="50%" height="50%">
</div>

## 1 环境准备
### 1.1 安装rust 
参考 [https://forge.rust-lang.org/infra/other-installation-methods.html#which](https://forge.rust-lang.org/infra/other-installation-methods.html#which)

使用了国内的镜像，出现选项直接Enter

```
export RUSTUP_DIST_SERVER=https://mirrors.ustc.edu.cn/rust-static
export RUSTUP_UPDATE_ROOT=https://mirrors.ustc.edu.cn/rust-static/rustup
curl --proto '=https' --tlsv1.2 -sSf https://mirrors.ustc.edu.cn/misc/rustup-install.sh | sh
```

### 1.2 安装Android Studio
参考 [https://developer.android.com/studio](https://developer.android.com/studio)

```
mkdir -p /root/android && cd /root/android
wget https://redirector.gvt1.com/edgedl/android/studio/ide-zips/2024.1.1.12/android-studio-2024.1.1.12-linux.tar.gz
tar -xvzf android-studio-2024.1.1.12-linux.tar.gz
cd android-studio
wget https://dl.google.com/android/repository/commandlinetools-linux-11076708_latest.zip?hl=zh-cn
unzip commandlinetools-linux-11076708_latest.zip\?hl\=zh-cn
export JAVA_HOME=/root/Downloads/android-studio/jbr
cmdline-tools/bin/sdkmanager "ndk;27.0.12077973" "cmake;3.22.1" "platforms;android-34" "build-tools;33.0.1" --sdk_root='sdk'
```


### 1.3 设置环境变量
```
. "$HOME/.cargo/env"
export ANDROID_NDK=/root/android/android-studio/sdk/ndk/27.0.12077973
export TVM_NDK_CC=$ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64/bin/aarch64-linux-android24-clang
export JAVA_HOME=/root/android//android-studio/jbr
export ANDROID_HOME=/root/android/android-studio/sdk
export PATH=/usr/local/cuda-12/bin:$PATH
export PATH=/root/android/android-studio/sdk/cmake/3.22.1/bin:$PATH
```
## 2 转换模型
### 2.1 安装mlc-llm
参考[https://llm.mlc.ai/docs/install/mlc_llm.html](https://llm.mlc.ai/docs/install/mlc_llm.html)
（如果下载很慢可以取消重新运行一下，或者本地下载了拷过去）
```
conda create --name mlc-prebuilt python=3.11
conda activate mlc-prebuilt
conda install -c conda-forge git-lfs
pip install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 transformers sentencepiece protobuf
wget https://github.com/mlc-ai/package/releases/download/v0.9.dev0/mlc_llm_nightly_cu122-0.1.dev1445-cp311-cp311-manylinux_2_28_x86_64.whl
wget https://github.com/mlc-ai/package/releases/download/v0.9.dev0/mlc_ai_nightly_cu122-0.15.dev404-cp311-cp311-manylinux_2_28_x86_64.whl
pip install mlc_ai_nightly_cu122-0.15.dev404-cp311-cp311-manylinux_2_28_x86_64.whl
pip install mlc_llm_nightly_cu122-0.1.dev1445-cp311-cp311-manylinux_2_28_x86_64.whl
```
测试如下输出说明安装正确

```
python -c "import mlc_llm; print(mlc_llm)"
```
![image](https://github.com/user-attachments/assets/f497f704-de19-4043-b901-af26856a62c9)

克隆项目
```
git clone https://github.com/mlc-ai/mlc-llm.git
cd mlc-llm
git submodule update --init --recursive
```

### 2.2 转换参数
You can be under the mlc-llm repo, or your own working directory. Note that all platforms can share the same compiled/quantized weights. See [Compile Command Specification](https://llm.mlc.ai/docs/compilation/compile_models.html#compile-command-specification) for specification of `convert_weight`.

```
cd android/MLCChat 
export TVM_SOURCE_DIR=/root/android/mlc-llm/3rdparty/tvm
export MLC_LLM_SOURCE_DIR=/root/android/mlc-llm
mlc_llm convert_weight /root/models/internlm2-chat-1_8b-sft/ \
 --quantization q4f16_1 \
 -o dist/internlm2-chat-1_8b-sft-q4f16_1-MLC
```
### 2.3 生成配置
Use mlc_llm gen_config to generate mlc-chat-config.json and process tokenizers. See [Compile Command Specification](https://llm.mlc.ai/docs/compilation/compile_models.html#compile-command-specification) for specification of `gen_config`.

```

mlc_llm gen_config /root/models/internlm2-chat-1_8b-sft/ \
 --quantization q4f16_1 --conv-template chatml \
 -o dist/internlm2-chat-1_8b-sft-q4f16_1-MLC

```
### 2.4 上传到huggingface
上传这一步需要能访问huggingface，可能需要部署代理
如果没有代理可以直接在接下来的配置中使用如下链接的模型（和文档中的转换方法一样）
[https://huggingface.co/timws/internlm2-chat-1_8b-sft-q4f16_1-MLC](https://huggingface.co/timws/internlm2-chat-1_8b-sft-q4f16_1-MLC)
### 2.5 (可选) 测试转换的模型
在打包之前可以测试模型效果，需要编译成二进制文件
在个人电脑上运行测试代码正常，**InternStudio**上**暂未成功**
```

mlc_llm compile ./dist/internlm2-chat-1_8b-sft-q4f16_1-MLC/mlc-chat-config.json \
 --device cuda -o dist/libs/internlm2-chat-1_8b-sft-q4f16_1-MLC-cuda.so
```
测试编译的模型是否符合预期，手机端运行的效果和测试效果接近
```python3
from mlc_llm import MLCEngine

# Create engine
engine = MLCEngine(model="./dist/internlm2-1_8b-q4f16_1-MLC", model_lib="./dist/libs/internlm2-1_8b-q4f16_1-MLC-cuda.so")

# Run chat completion in OpenAI API.
print(engine)
for response in engine.chat.completions.create(
 messages=[{"role": "user", "content": "你是谁？"}],
 stream=True
):
 for choice in response.choices:
 print(choice.delta.content, end="", flush=True)
print("\n")
engine.terminate()
```

## 3 打包运行
### 3.1 修改配置文件
修改`mlc-package-config.json`
参考如下
```
{
 "device": "android",
 "model_list": [
 {
 "model": "HF://timws/internlm2-chat-1_8b-sft-q4f16_1-MLC",
 "estimated_vram_bytes": 3980990464,
 "model_id": "internlm2-chat-1_8b-sft-q4f16_1-MLC"

 },
 {
 "model": "HF://mlc-ai/gemma-2b-it-q4f16_1-MLC",
 "model_id": "gemma-2b-q4f16_1-MLC",
 "estimated_vram_bytes": 3980990464
 }
 ]
}

```

### 3.2 运行打包命令

```
 mlc_llm package
```
![image](https://github.com/user-attachments/assets/0e1db9c5-6252-4c6f-81b2-a739fa4fac44)


### 3.3 创建签名
```
cd /root/android/mlc-llm/android/MLCChat
/root/android/android-studio/jbr/bin/keytool -genkey -v -keystore my-release-key.jks -keyalg RSA -keysize 2048 -validity 10000
Enter keystore password: 
Re-enter new password: 
What is your first and last name?
 [Unknown]: Any
What is the name of your organizational unit?
 [Unknown]: Any
What is the name of your organization?
 [Unknown]: Any
What is the name of your City or Locality?
 [Unknown]: Any
What is the name of your State or Province?
 [Unknown]: Any
What is the two-letter country code for this unit?
 [Unknown]: CN
Is CN=Any, OU=Any, O=Any, L=Any, ST=Any, C=CN correct?
 [no]: yes

Generating 2,048 bit RSA key pair and self-signed certificate (SHA256withRSA) with a validity of 10,000 days
 for: CN=Any, OU=Any, O=Any, L=Any, ST=Any, C=CN
[Storing my-release-key.jks]
```
### 3.4 修改gradle配置
如果是本地可以WIFI或USB调试不用签名，在服务器构建需要签名
修改`app/build.gradle`为如下内容，主要是增加了签名部分，注意确认签名文件的位置
```
plugins {
 id 'com.android.application'
 id 'org.jetbrains.kotlin.android'
}

android {
 namespace 'ai.mlc.mlcchat'
 compileSdk 34

 defaultConfig {
 applicationId "ai.mlc.mlcchat"
 minSdk 26
 targetSdk 33
 versionCode 1
 versionName "1.0"

 testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
 vectorDrawables {
 useSupportLibrary true
 }
 }


 compileOptions {
 sourceCompatibility JavaVersion.VERSION_1_8
 targetCompatibility JavaVersion.VERSION_1_8
 }
 kotlinOptions {
 jvmTarget = '1.8'
 }
 buildFeatures {
 compose true
 }
 composeOptions {
 kotlinCompilerExtensionVersion '1.4.3'
 }
 packagingOptions {
 resources {
 excludes += '/META-INF/{AL2.0,LGPL2.1}'
 }
 }

 signingConfigs {
 release {
 storeFile file("/root/android/mlc-llm/android/MLCChat/my-release-key.jks")
 storePassword "123456"
 keyAlias "mykey"
 keyPassword "123456"
 }
 }

 buildTypes {
 release {
 minifyEnabled false
 proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
 signingConfig signingConfigs.release
 }
 }
}

dependencies {
 implementation project(":mlc4j")
 implementation 'androidx.core:core-ktx:1.10.1'
 implementation 'androidx.lifecycle:lifecycle-runtime-ktx:2.6.1'
 implementation 'androidx.activity:activity-compose:1.7.1'
 implementation platform('androidx.compose:compose-bom:2022.10.00')
 implementation 'androidx.lifecycle:lifecycle-viewmodel-compose:2.6.1'
 implementation 'androidx.compose.ui:ui'
 implementation 'androidx.compose.ui:ui-graphics'
 implementation 'androidx.compose.ui:ui-tooling-preview'
 implementation 'androidx.compose.material3:material3:1.1.0'
 implementation 'androidx.compose.material:material-icons-extended'
 implementation 'androidx.appcompat:appcompat:1.6.1'
 implementation 'androidx.navigation:navigation-compose:2.5.3'
 implementation 'com.google.code.gson:gson:2.10.1'
 implementation fileTree(dir: 'src/main/libs', include: ['*.aar', '*.jar'], exclude: [])
 testImplementation 'junit:junit:4.13.2'
 androidTestImplementation 'androidx.test.ext:junit:1.1.5'
 androidTestImplementation 'androidx.test.espresso:espresso-core:3.5.1'
 androidTestImplementation platform('androidx.compose:compose-bom:2022.10.00')
 androidTestImplementation 'androidx.compose.ui:ui-test-junit4'
 debugImplementation 'androidx.compose.ui:ui-tooling'
 debugImplementation 'androidx.compose.ui:ui-test-manifest'

}
```

### 3.5 命令行编译
运行编译命令，完成后在`app/build/outputs/apk/release`生成`app-release.apk`安装包，下载到手机上运行
运行App需要能访问huggingface下载模型(参考文档中的bundle方法需要ADB刷入模型数据)
```
./gradlew assembleRelease
```
![image](https://github.com/user-attachments/assets/df7673f7-7128-4079-b77d-37093fff6660)



### 3.6 运行体验
运行App需要能访问huggingface下载模型

<div align="center">
 <img src="https://github.com/user-attachments/assets/cd9fe502-490e-40c6-8649-30671a4fe504" width="50%" height="50%">
</div>


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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 Merged

vansin
merged 2 commits into

from

Aug 2, 2024
 Merged


add tutorial for android
#1448
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6c3289d

add tutorial for android


qew21 Aug 1, 2024
d3d2055

Update layout


qew21 Aug 1, 2024

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Original file line numberDiff line numberDiff line change
@@ -1,2 +1,291 @@
# InternLM 1.8B 安卓端部署实践
![image](https://github.com/user-attachments/assets/5ede99d5-e82b-4ff8-acd8-407f9277967a)

本文将带大家手把手使用[mlc-llm](https://llm.mlc.ai/docs/deploy/android.html#android-sdk)将 InternLM2 部署到安卓手机上

首先我们来看一下最终的效果～
<div align="center">
 <img src="https://github.com/user-attachments/assets/cd9fe502-490e-40c6-8649-30671a4fe504" width="50%" height="50%">
</div>

## 1 环境准备
### 1.1 安装rust 
参考 [https://forge.rust-lang.org/infra/other-installation-methods.html#which](https://forge.rust-lang.org/infra/other-installation-methods.html#which)

使用了国内的镜像，出现选项直接Enter

```
export RUSTUP_DIST_SERVER=https://mirrors.ustc.edu.cn/rust-static
export RUSTUP_UPDATE_ROOT=https://mirrors.ustc.edu.cn/rust-static/rustup
curl --proto '=https' --tlsv1.2 -sSf https://mirrors.ustc.edu.cn/misc/rustup-install.sh | sh
```

### 1.2 安装Android Studio
参考 [https://developer.android.com/studio](https://developer.android.com/studio)

```
mkdir -p /root/android && cd /root/android
wget https://redirector.gvt1.com/edgedl/android/studio/ide-zips/2024.1.1.12/android-studio-2024.1.1.12-linux.tar.gz
tar -xvzf android-studio-2024.1.1.12-linux.tar.gz
cd android-studio
wget https://dl.google.com/android/repository/commandlinetools-linux-11076708_latest.zip?hl=zh-cn
unzip commandlinetools-linux-11076708_latest.zip\?hl\=zh-cn
export JAVA_HOME=/root/Downloads/android-studio/jbr
cmdline-tools/bin/sdkmanager "ndk;27.0.12077973" "cmake;3.22.1" "platforms;android-34" "build-tools;33.0.1" --sdk_root='sdk'
```


### 1.3 设置环境变量
```
. "$HOME/.cargo/env"
export ANDROID_NDK=/root/android/android-studio/sdk/ndk/27.0.12077973
export TVM_NDK_CC=$ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64/bin/aarch64-linux-android24-clang
export JAVA_HOME=/root/android//android-studio/jbr
export ANDROID_HOME=/root/android/android-studio/sdk
export PATH=/usr/local/cuda-12/bin:$PATH
export PATH=/root/android/android-studio/sdk/cmake/3.22.1/bin:$PATH
```
## 2 转换模型
### 2.1 安装mlc-llm
参考[https://llm.mlc.ai/docs/install/mlc_llm.html](https://llm.mlc.ai/docs/install/mlc_llm.html)
（如果下载很慢可以取消重新运行一下，或者本地下载了拷过去）
```
conda create --name mlc-prebuilt python=3.11
conda activate mlc-prebuilt
conda install -c conda-forge git-lfs
pip install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 transformers sentencepiece protobuf
wget https://github.com/mlc-ai/package/releases/download/v0.9.dev0/mlc_llm_nightly_cu122-0.1.dev1445-cp311-cp311-manylinux_2_28_x86_64.whl
wget https://github.com/mlc-ai/package/releases/download/v0.9.dev0/mlc_ai_nightly_cu122-0.15.dev404-cp311-cp311-manylinux_2_28_x86_64.whl
pip install mlc_ai_nightly_cu122-0.15.dev404-cp311-cp311-manylinux_2_28_x86_64.whl
pip install mlc_llm_nightly_cu122-0.1.dev1445-cp311-cp311-manylinux_2_28_x86_64.whl
```
测试如下输出说明安装正确

```
python -c "import mlc_llm; print(mlc_llm)"
```
![image](https://github.com/user-attachments/assets/f497f704-de19-4043-b901-af26856a62c9)

克隆项目
```
git clone https://github.com/mlc-ai/mlc-llm.git
cd mlc-llm
git submodule update --init --recursive
```

### 2.2 转换参数
You can be under the mlc-llm repo, or your own working directory. Note that all platforms can share the same compiled/quantized weights. See [Compile Command Specification](https://llm.mlc.ai/docs/compilation/compile_models.html#compile-command-specification) for specification of `convert_weight`.

```
cd android/MLCChat 
export TVM_SOURCE_DIR=/root/android/mlc-llm/3rdparty/tvm
export MLC_LLM_SOURCE_DIR=/root/android/mlc-llm
mlc_llm convert_weight /root/models/internlm2-chat-1_8b-sft/ \
 --quantization q4f16_1 \
 -o dist/internlm2-chat-1_8b-sft-q4f16_1-MLC
```
### 2.3 生成配置
Use mlc_llm gen_config to generate mlc-chat-config.json and process tokenizers. See [Compile Command Specification](https://llm.mlc.ai/docs/compilation/compile_models.html#compile-command-specification) for specification of `gen_config`.

```

mlc_llm gen_config /root/models/internlm2-chat-1_8b-sft/ \
 --quantization q4f16_1 --conv-template chatml \
 -o dist/internlm2-chat-1_8b-sft-q4f16_1-MLC

```
### 2.4 上传到huggingface
上传这一步需要能访问huggingface，可能需要部署代理
如果没有代理可以直接在接下来的配置中使用如下链接的模型（和文档中的转换方法一样）
[https://huggingface.co/timws/internlm2-chat-1_8b-sft-q4f16_1-MLC](https://huggingface.co/timws/internlm2-chat-1_8b-sft-q4f16_1-MLC)
### 2.5 (可选) 测试转换的模型
在打包之前可以测试模型效果，需要编译成二进制文件
在个人电脑上运行测试代码正常，**InternStudio**上**暂未成功**
```

mlc_llm compile ./dist/internlm2-chat-1_8b-sft-q4f16_1-MLC/mlc-chat-config.json \
 --device cuda -o dist/libs/internlm2-chat-1_8b-sft-q4f16_1-MLC-cuda.so
```
测试编译的模型是否符合预期，手机端运行的效果和测试效果接近
```python3
from mlc_llm import MLCEngine

# Create engine
engine = MLCEngine(model="./dist/internlm2-1_8b-q4f16_1-MLC", model_lib="./dist/libs/internlm2-1_8b-q4f16_1-MLC-cuda.so")

# Run chat completion in OpenAI API.
print(engine)
for response in engine.chat.completions.create(
 messages=[{"role": "user", "content": "你是谁？"}],
 stream=True
):
 for choice in response.choices:
 print(choice.delta.content, end="", flush=True)
print("\n")
engine.terminate()
```

## 3 打包运行
### 3.1 修改配置文件
修改`mlc-package-config.json`
参考如下
```
{
 "device": "android",
 "model_list": [
 {
 "model": "HF://timws/internlm2-chat-1_8b-sft-q4f16_1-MLC",
 "estimated_vram_bytes": 3980990464,
 "model_id": "internlm2-chat-1_8b-sft-q4f16_1-MLC"

 },
 {
 "model": "HF://mlc-ai/gemma-2b-it-q4f16_1-MLC",
 "model_id": "gemma-2b-q4f16_1-MLC",
 "estimated_vram_bytes": 3980990464
 }
 ]
}

```

### 3.2 运行打包命令

```
 mlc_llm package
```
![image](https://github.com/user-attachments/assets/0e1db9c5-6252-4c6f-81b2-a739fa4fac44)


### 3.3 创建签名
```
cd /root/android/mlc-llm/android/MLCChat
/root/android/android-studio/jbr/bin/keytool -genkey -v -keystore my-release-key.jks -keyalg RSA -keysize 2048 -validity 10000
Enter keystore password: 
Re-enter new password: 
What is your first and last name?
 [Unknown]: Any
What is the name of your organizational unit?
 [Unknown]: Any
What is the name of your organization?
 [Unknown]: Any
What is the name of your City or Locality?
 [Unknown]: Any
What is the name of your State or Province?
 [Unknown]: Any
What is the two-letter country code for this unit?
 [Unknown]: CN
Is CN=Any, OU=Any, O=Any, L=Any, ST=Any, C=CN correct?
 [no]: yes

Generating 2,048 bit RSA key pair and self-signed certificate (SHA256withRSA) with a validity of 10,000 days
 for: CN=Any, OU=Any, O=Any, L=Any, ST=Any, C=CN
[Storing my-release-key.jks]
```
### 3.4 修改gradle配置
如果是本地可以WIFI或USB调试不用签名，在服务器构建需要签名
修改`app/build.gradle`为如下内容，主要是增加了签名部分，注意确认签名文件的位置
```
plugins {
 id 'com.android.application'
 id 'org.jetbrains.kotlin.android'
}

android {
 namespace 'ai.mlc.mlcchat'
 compileSdk 34

 defaultConfig {
 applicationId "ai.mlc.mlcchat"
 minSdk 26
 targetSdk 33
 versionCode 1
 versionName "1.0"

 testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
 vectorDrawables {
 useSupportLibrary true
 }
 }


 compileOptions {
 sourceCompatibility JavaVersion.VERSION_1_8
 targetCompatibility JavaVersion.VERSION_1_8
 }
 kotlinOptions {
 jvmTarget = '1.8'
 }
 buildFeatures {
 compose true
 }
 composeOptions {
 kotlinCompilerExtensionVersion '1.4.3'
 }
 packagingOptions {
 resources {
 excludes += '/META-INF/{AL2.0,LGPL2.1}'
 }
 }

 signingConfigs {
 release {
 storeFile file("/root/android/mlc-llm/android/MLCChat/my-release-key.jks")
 storePassword "123456"
 keyAlias "mykey"
 keyPassword "123456"
 }
 }

 buildTypes {
 release {
 minifyEnabled false
 proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
 signingConfig signingConfigs.release
 }
 }
}

dependencies {
 implementation project(":mlc4j")
 implementation 'androidx.core:core-ktx:1.10.1'
 implementation 'androidx.lifecycle:lifecycle-runtime-ktx:2.6.1'
 implementation 'androidx.activity:activity-compose:1.7.1'
 implementation platform('androidx.compose:compose-bom:2022.10.00')
 implementation 'androidx.lifecycle:lifecycle-viewmodel-compose:2.6.1'
 implementation 'androidx.compose.ui:ui'
 implementation 'androidx.compose.ui:ui-graphics'
 implementation 'androidx.compose.ui:ui-tooling-preview'
 implementation 'androidx.compose.material3:material3:1.1.0'
 implementation 'androidx.compose.material:material-icons-extended'
 implementation 'androidx.appcompat:appcompat:1.6.1'
 implementation 'androidx.navigation:navigation-compose:2.5.3'
 implementation 'com.google.code.gson:gson:2.10.1'
 implementation fileTree(dir: 'src/main/libs', include: ['*.aar', '*.jar'], exclude: [])
 testImplementation 'junit:junit:4.13.2'
 androidTestImplementation 'androidx.test.ext:junit:1.1.5'
 androidTestImplementation 'androidx.test.espresso:espresso-core:3.5.1'
 androidTestImplementation platform('androidx.compose:compose-bom:2022.10.00')
 androidTestImplementation 'androidx.compose.ui:ui-test-junit4'
 debugImplementation 'androidx.compose.ui:ui-tooling'
 debugImplementation 'androidx.compose.ui:ui-test-manifest'

}
```

### 3.5 命令行编译
运行编译命令，完成后在`app/build/outputs/apk/release`生成`app-release.apk`安装包，下载到手机上运行
运行App需要能访问huggingface下载模型(参考文档中的bundle方法需要ADB刷入模型数据)
```
./gradlew assembleRelease
```
![image](https://github.com/user-attachments/assets/df7673f7-7128-4079-b77d-37093fff6660)



### 3.6 运行体验
运行App需要能访问huggingface下载模型

<div align="center">
 <img src="https://github.com/user-attachments/assets/cd9fe502-490e-40c6-8649-30671a4fe504" width="50%" height="50%">
</div>


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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 Merged

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docs/EasterEgg/Android/readme.md

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# InternLM 1.8B 安卓端部署实践
![image](https://github.com/user-attachments/assets/5ede99d5-e82b-4ff8-acd8-407f9277967a)

本文将带大家手把手使用[mlc-llm](https://llm.mlc.ai/docs/deploy/android.html#android-sdk)将 InternLM2 部署到安卓手机上

首先我们来看一下最终的效果～
<div align="center">
 <img src="https://github.com/user-attachments/assets/cd9fe502-490e-40c6-8649-30671a4fe504" width="50%" height="50%">
</div>

## 1 环境准备
### 1.1 安装rust 
参考 [https://forge.rust-lang.org/infra/other-installation-methods.html#which](https://forge.rust-lang.org/infra/other-installation-methods.html#which)

使用了国内的镜像，出现选项直接Enter

```
export RUSTUP_DIST_SERVER=https://mirrors.ustc.edu.cn/rust-static
export RUSTUP_UPDATE_ROOT=https://mirrors.ustc.edu.cn/rust-static/rustup
curl --proto '=https' --tlsv1.2 -sSf https://mirrors.ustc.edu.cn/misc/rustup-install.sh | sh
```

### 1.2 安装Android Studio
参考 [https://developer.android.com/studio](https://developer.android.com/studio)

```
mkdir -p /root/android && cd /root/android
wget https://redirector.gvt1.com/edgedl/android/studio/ide-zips/2024.1.1.12/android-studio-2024.1.1.12-linux.tar.gz
tar -xvzf android-studio-2024.1.1.12-linux.tar.gz
cd android-studio
wget https://dl.google.com/android/repository/commandlinetools-linux-11076708_latest.zip?hl=zh-cn
unzip commandlinetools-linux-11076708_latest.zip\?hl\=zh-cn
export JAVA_HOME=/root/Downloads/android-studio/jbr
cmdline-tools/bin/sdkmanager "ndk;27.0.12077973" "cmake;3.22.1" "platforms;android-34" "build-tools;33.0.1" --sdk_root='sdk'
```


### 1.3 设置环境变量
```
. "$HOME/.cargo/env"
export ANDROID_NDK=/root/android/android-studio/sdk/ndk/27.0.12077973
export TVM_NDK_CC=$ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64/bin/aarch64-linux-android24-clang
export JAVA_HOME=/root/android//android-studio/jbr
export ANDROID_HOME=/root/android/android-studio/sdk
export PATH=/usr/local/cuda-12/bin:$PATH
export PATH=/root/android/android-studio/sdk/cmake/3.22.1/bin:$PATH
```
## 2 转换模型
### 2.1 安装mlc-llm
参考[https://llm.mlc.ai/docs/install/mlc_llm.html](https://llm.mlc.ai/docs/install/mlc_llm.html)
（如果下载很慢可以取消重新运行一下，或者本地下载了拷过去）
```
conda create --name mlc-prebuilt python=3.11
conda activate mlc-prebuilt
conda install -c conda-forge git-lfs
pip install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 transformers sentencepiece protobuf
wget https://github.com/mlc-ai/package/releases/download/v0.9.dev0/mlc_llm_nightly_cu122-0.1.dev1445-cp311-cp311-manylinux_2_28_x86_64.whl
wget https://github.com/mlc-ai/package/releases/download/v0.9.dev0/mlc_ai_nightly_cu122-0.15.dev404-cp311-cp311-manylinux_2_28_x86_64.whl
pip install mlc_ai_nightly_cu122-0.15.dev404-cp311-cp311-manylinux_2_28_x86_64.whl
pip install mlc_llm_nightly_cu122-0.1.dev1445-cp311-cp311-manylinux_2_28_x86_64.whl
```
测试如下输出说明安装正确

```
python -c "import mlc_llm; print(mlc_llm)"
```
![image](https://github.com/user-attachments/assets/f497f704-de19-4043-b901-af26856a62c9)

克隆项目
```
git clone https://github.com/mlc-ai/mlc-llm.git
cd mlc-llm
git submodule update --init --recursive
```

### 2.2 转换参数
You can be under the mlc-llm repo, or your own working directory. Note that all platforms can share the same compiled/quantized weights. See [Compile Command Specification](https://llm.mlc.ai/docs/compilation/compile_models.html#compile-command-specification) for specification of `convert_weight`.

```
cd android/MLCChat 
export TVM_SOURCE_DIR=/root/android/mlc-llm/3rdparty/tvm
export MLC_LLM_SOURCE_DIR=/root/android/mlc-llm
mlc_llm convert_weight /root/models/internlm2-chat-1_8b-sft/ \
 --quantization q4f16_1 \
 -o dist/internlm2-chat-1_8b-sft-q4f16_1-MLC
```
### 2.3 生成配置
Use mlc_llm gen_config to generate mlc-chat-config.json and process tokenizers. See [Compile Command Specification](https://llm.mlc.ai/docs/compilation/compile_models.html#compile-command-specification) for specification of `gen_config`.

```

mlc_llm gen_config /root/models/internlm2-chat-1_8b-sft/ \
 --quantization q4f16_1 --conv-template chatml \
 -o dist/internlm2-chat-1_8b-sft-q4f16_1-MLC

```
### 2.4 上传到huggingface
上传这一步需要能访问huggingface，可能需要部署代理
如果没有代理可以直接在接下来的配置中使用如下链接的模型（和文档中的转换方法一样）
[https://huggingface.co/timws/internlm2-chat-1_8b-sft-q4f16_1-MLC](https://huggingface.co/timws/internlm2-chat-1_8b-sft-q4f16_1-MLC)
### 2.5 (可选) 测试转换的模型
在打包之前可以测试模型效果，需要编译成二进制文件
在个人电脑上运行测试代码正常，**InternStudio**上**暂未成功**
```

mlc_llm compile ./dist/internlm2-chat-1_8b-sft-q4f16_1-MLC/mlc-chat-config.json \
 --device cuda -o dist/libs/internlm2-chat-1_8b-sft-q4f16_1-MLC-cuda.so
```
测试编译的模型是否符合预期，手机端运行的效果和测试效果接近
```python3
from mlc_llm import MLCEngine

# Create engine
engine = MLCEngine(model="./dist/internlm2-1_8b-q4f16_1-MLC", model_lib="./dist/libs/internlm2-1_8b-q4f16_1-MLC-cuda.so")

# Run chat completion in OpenAI API.
print(engine)
for response in engine.chat.completions.create(
 messages=[{"role": "user", "content": "你是谁？"}],
 stream=True
):
 for choice in response.choices:
 print(choice.delta.content, end="", flush=True)
print("\n")
engine.terminate()
```

## 3 打包运行
### 3.1 修改配置文件
修改`mlc-package-config.json`
参考如下
```
{
 "device": "android",
 "model_list": [
 {
 "model": "HF://timws/internlm2-chat-1_8b-sft-q4f16_1-MLC",
 "estimated_vram_bytes": 3980990464,
 "model_id": "internlm2-chat-1_8b-sft-q4f16_1-MLC"

 },
 {
 "model": "HF://mlc-ai/gemma-2b-it-q4f16_1-MLC",
 "model_id": "gemma-2b-q4f16_1-MLC",
 "estimated_vram_bytes": 3980990464
 }
 ]
}

```

### 3.2 运行打包命令

```
 mlc_llm package
```
![image](https://github.com/user-attachments/assets/0e1db9c5-6252-4c6f-81b2-a739fa4fac44)


### 3.3 创建签名
```
cd /root/android/mlc-llm/android/MLCChat
/root/android/android-studio/jbr/bin/keytool -genkey -v -keystore my-release-key.jks -keyalg RSA -keysize 2048 -validity 10000
Enter keystore password: 
Re-enter new password: 
What is your first and last name?
 [Unknown]: Any
What is the name of your organizational unit?
 [Unknown]: Any
What is the name of your organization?
 [Unknown]: Any
What is the name of your City or Locality?
 [Unknown]: Any
What is the name of your State or Province?
 [Unknown]: Any
What is the two-letter country code for this unit?
 [Unknown]: CN
Is CN=Any, OU=Any, O=Any, L=Any, ST=Any, C=CN correct?
 [no]: yes

Generating 2,048 bit RSA key pair and self-signed certificate (SHA256withRSA) with a validity of 10,000 days
 for: CN=Any, OU=Any, O=Any, L=Any, ST=Any, C=CN
[Storing my-release-key.jks]
```
### 3.4 修改gradle配置
如果是本地可以WIFI或USB调试不用签名，在服务器构建需要签名
修改`app/build.gradle`为如下内容，主要是增加了签名部分，注意确认签名文件的位置
```
plugins {
 id 'com.android.application'
 id 'org.jetbrains.kotlin.android'
}

android {
 namespace 'ai.mlc.mlcchat'
 compileSdk 34

 defaultConfig {
 applicationId "ai.mlc.mlcchat"
 minSdk 26
 targetSdk 33
 versionCode 1
 versionName "1.0"

 testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
 vectorDrawables {
 useSupportLibrary true
 }
 }


 compileOptions {
 sourceCompatibility JavaVersion.VERSION_1_8
 targetCompatibility JavaVersion.VERSION_1_8
 }
 kotlinOptions {
 jvmTarget = '1.8'
 }
 buildFeatures {
 compose true
 }
 composeOptions {
 kotlinCompilerExtensionVersion '1.4.3'
 }
 packagingOptions {
 resources {
 excludes += '/META-INF/{AL2.0,LGPL2.1}'
 }
 }

 signingConfigs {
 release {
 storeFile file("/root/android/mlc-llm/android/MLCChat/my-release-key.jks")
 storePassword "123456"
 keyAlias "mykey"
 keyPassword "123456"
 }
 }

 buildTypes {
 release {
 minifyEnabled false
 proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
 signingConfig signingConfigs.release
 }
 }
}

dependencies {
 implementation project(":mlc4j")
 implementation 'androidx.core:core-ktx:1.10.1'
 implementation 'androidx.lifecycle:lifecycle-runtime-ktx:2.6.1'
 implementation 'androidx.activity:activity-compose:1.7.1'
 implementation platform('androidx.compose:compose-bom:2022.10.00')
 implementation 'androidx.lifecycle:lifecycle-viewmodel-compose:2.6.1'
 implementation 'androidx.compose.ui:ui'
 implementation 'androidx.compose.ui:ui-graphics'
 implementation 'androidx.compose.ui:ui-tooling-preview'
 implementation 'androidx.compose.material3:material3:1.1.0'
 implementation 'androidx.compose.material:material-icons-extended'
 implementation 'androidx.appcompat:appcompat:1.6.1'
 implementation 'androidx.navigation:navigation-compose:2.5.3'
 implementation 'com.google.code.gson:gson:2.10.1'
 implementation fileTree(dir: 'src/main/libs', include: ['*.aar', '*.jar'], exclude: [])
 testImplementation 'junit:junit:4.13.2'
 androidTestImplementation 'androidx.test.ext:junit:1.1.5'
 androidTestImplementation 'androidx.test.espresso:espresso-core:3.5.1'
 androidTestImplementation platform('androidx.compose:compose-bom:2022.10.00')
 androidTestImplementation 'androidx.compose.ui:ui-test-junit4'
 debugImplementation 'androidx.compose.ui:ui-tooling'
 debugImplementation 'androidx.compose.ui:ui-test-manifest'

}
```

### 3.5 命令行编译
运行编译命令，完成后在`app/build/outputs/apk/release`生成`app-release.apk`安装包，下载到手机上运行
运行App需要能访问huggingface下载模型(参考文档中的bundle方法需要ADB刷入模型数据)
```
./gradlew assembleRelease
```
![image](https://github.com/user-attachments/assets/df7673f7-7128-4079-b77d-37093fff6660)



### 3.6 运行体验
运行App需要能访问huggingface下载模型

<div align="center">
 <img src="https://github.com/user-attachments/assets/cd9fe502-490e-40c6-8649-30671a4fe504" width="50%" height="50%">
</div>


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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# InternLM 1.8B 安卓端部署实践
![image](https://github.com/user-attachments/assets/5ede99d5-e82b-4ff8-acd8-407f9277967a)

本文将带大家手把手使用[mlc-llm](https://llm.mlc.ai/docs/deploy/android.html#android-sdk)将 InternLM2 部署到安卓手机上

首先我们来看一下最终的效果～
<div align="center">
 <img src="https://github.com/user-attachments/assets/cd9fe502-490e-40c6-8649-30671a4fe504" width="50%" height="50%">
</div>

## 1 环境准备
### 1.1 安装rust 
参考 [https://forge.rust-lang.org/infra/other-installation-methods.html#which](https://forge.rust-lang.org/infra/other-installation-methods.html#which)

使用了国内的镜像，出现选项直接Enter

```
export RUSTUP_DIST_SERVER=https://mirrors.ustc.edu.cn/rust-static
export RUSTUP_UPDATE_ROOT=https://mirrors.ustc.edu.cn/rust-static/rustup
curl --proto '=https' --tlsv1.2 -sSf https://mirrors.ustc.edu.cn/misc/rustup-install.sh | sh
```

### 1.2 安装Android Studio
参考 [https://developer.android.com/studio](https://developer.android.com/studio)

```
mkdir -p /root/android && cd /root/android
wget https://redirector.gvt1.com/edgedl/android/studio/ide-zips/2024.1.1.12/android-studio-2024.1.1.12-linux.tar.gz
tar -xvzf android-studio-2024.1.1.12-linux.tar.gz
cd android-studio
wget https://dl.google.com/android/repository/commandlinetools-linux-11076708_latest.zip?hl=zh-cn
unzip commandlinetools-linux-11076708_latest.zip\?hl\=zh-cn
export JAVA_HOME=/root/Downloads/android-studio/jbr
cmdline-tools/bin/sdkmanager "ndk;27.0.12077973" "cmake;3.22.1" "platforms;android-34" "build-tools;33.0.1" --sdk_root='sdk'
```


### 1.3 设置环境变量
```
. "$HOME/.cargo/env"
export ANDROID_NDK=/root/android/android-studio/sdk/ndk/27.0.12077973
export TVM_NDK_CC=$ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64/bin/aarch64-linux-android24-clang
export JAVA_HOME=/root/android//android-studio/jbr
export ANDROID_HOME=/root/android/android-studio/sdk
export PATH=/usr/local/cuda-12/bin:$PATH
export PATH=/root/android/android-studio/sdk/cmake/3.22.1/bin:$PATH
```
## 2 转换模型
### 2.1 安装mlc-llm
参考[https://llm.mlc.ai/docs/install/mlc_llm.html](https://llm.mlc.ai/docs/install/mlc_llm.html)
（如果下载很慢可以取消重新运行一下，或者本地下载了拷过去）
```
conda create --name mlc-prebuilt python=3.11
conda activate mlc-prebuilt
conda install -c conda-forge git-lfs
pip install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 transformers sentencepiece protobuf
wget https://github.com/mlc-ai/package/releases/download/v0.9.dev0/mlc_llm_nightly_cu122-0.1.dev1445-cp311-cp311-manylinux_2_28_x86_64.whl
wget https://github.com/mlc-ai/package/releases/download/v0.9.dev0/mlc_ai_nightly_cu122-0.15.dev404-cp311-cp311-manylinux_2_28_x86_64.whl
pip install mlc_ai_nightly_cu122-0.15.dev404-cp311-cp311-manylinux_2_28_x86_64.whl
pip install mlc_llm_nightly_cu122-0.1.dev1445-cp311-cp311-manylinux_2_28_x86_64.whl
```
测试如下输出说明安装正确

```
python -c "import mlc_llm; print(mlc_llm)"
```
![image](https://github.com/user-attachments/assets/f497f704-de19-4043-b901-af26856a62c9)

克隆项目
```
git clone https://github.com/mlc-ai/mlc-llm.git
cd mlc-llm
git submodule update --init --recursive
```

### 2.2 转换参数
You can be under the mlc-llm repo, or your own working directory. Note that all platforms can share the same compiled/quantized weights. See [Compile Command Specification](https://llm.mlc.ai/docs/compilation/compile_models.html#compile-command-specification) for specification of `convert_weight`.

```
cd android/MLCChat 
export TVM_SOURCE_DIR=/root/android/mlc-llm/3rdparty/tvm
export MLC_LLM_SOURCE_DIR=/root/android/mlc-llm
mlc_llm convert_weight /root/models/internlm2-chat-1_8b-sft/ \
 --quantization q4f16_1 \
 -o dist/internlm2-chat-1_8b-sft-q4f16_1-MLC
```
### 2.3 生成配置
Use mlc_llm gen_config to generate mlc-chat-config.json and process tokenizers. See [Compile Command Specification](https://llm.mlc.ai/docs/compilation/compile_models.html#compile-command-specification) for specification of `gen_config`.

```

mlc_llm gen_config /root/models/internlm2-chat-1_8b-sft/ \
 --quantization q4f16_1 --conv-template chatml \
 -o dist/internlm2-chat-1_8b-sft-q4f16_1-MLC

```
### 2.4 上传到huggingface
上传这一步需要能访问huggingface，可能需要部署代理
如果没有代理可以直接在接下来的配置中使用如下链接的模型（和文档中的转换方法一样）
[https://huggingface.co/timws/internlm2-chat-1_8b-sft-q4f16_1-MLC](https://huggingface.co/timws/internlm2-chat-1_8b-sft-q4f16_1-MLC)
### 2.5 (可选) 测试转换的模型
在打包之前可以测试模型效果，需要编译成二进制文件
在个人电脑上运行测试代码正常，**InternStudio**上**暂未成功**
```

mlc_llm compile ./dist/internlm2-chat-1_8b-sft-q4f16_1-MLC/mlc-chat-config.json \
 --device cuda -o dist/libs/internlm2-chat-1_8b-sft-q4f16_1-MLC-cuda.so
```
测试编译的模型是否符合预期，手机端运行的效果和测试效果接近
```python3
from mlc_llm import MLCEngine

# Create engine
engine = MLCEngine(model="./dist/internlm2-1_8b-q4f16_1-MLC", model_lib="./dist/libs/internlm2-1_8b-q4f16_1-MLC-cuda.so")

# Run chat completion in OpenAI API.
print(engine)
for response in engine.chat.completions.create(
 messages=[{"role": "user", "content": "你是谁？"}],
 stream=True
):
 for choice in response.choices:
 print(choice.delta.content, end="", flush=True)
print("\n")
engine.terminate()
```

## 3 打包运行
### 3.1 修改配置文件
修改`mlc-package-config.json`
参考如下
```
{
 "device": "android",
 "model_list": [
 {
 "model": "HF://timws/internlm2-chat-1_8b-sft-q4f16_1-MLC",
 "estimated_vram_bytes": 3980990464,
 "model_id": "internlm2-chat-1_8b-sft-q4f16_1-MLC"

 },
 {
 "model": "HF://mlc-ai/gemma-2b-it-q4f16_1-MLC",
 "model_id": "gemma-2b-q4f16_1-MLC",
 "estimated_vram_bytes": 3980990464
 }
 ]
}

```

### 3.2 运行打包命令

```
 mlc_llm package
```
![image](https://github.com/user-attachments/assets/0e1db9c5-6252-4c6f-81b2-a739fa4fac44)


### 3.3 创建签名
```
cd /root/android/mlc-llm/android/MLCChat
/root/android/android-studio/jbr/bin/keytool -genkey -v -keystore my-release-key.jks -keyalg RSA -keysize 2048 -validity 10000
Enter keystore password: 
Re-enter new password: 
What is your first and last name?
 [Unknown]: Any
What is the name of your organizational unit?
 [Unknown]: Any
What is the name of your organization?
 [Unknown]: Any
What is the name of your City or Locality?
 [Unknown]: Any
What is the name of your State or Province?
 [Unknown]: Any
What is the two-letter country code for this unit?
 [Unknown]: CN
Is CN=Any, OU=Any, O=Any, L=Any, ST=Any, C=CN correct?
 [no]: yes

Generating 2,048 bit RSA key pair and self-signed certificate (SHA256withRSA) with a validity of 10,000 days
 for: CN=Any, OU=Any, O=Any, L=Any, ST=Any, C=CN
[Storing my-release-key.jks]
```
### 3.4 修改gradle配置
如果是本地可以WIFI或USB调试不用签名，在服务器构建需要签名
修改`app/build.gradle`为如下内容，主要是增加了签名部分，注意确认签名文件的位置
```
plugins {
 id 'com.android.application'
 id 'org.jetbrains.kotlin.android'
}

android {
 namespace 'ai.mlc.mlcchat'
 compileSdk 34

 defaultConfig {
 applicationId "ai.mlc.mlcchat"
 minSdk 26
 targetSdk 33
 versionCode 1
 versionName "1.0"

 testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
 vectorDrawables {
 useSupportLibrary true
 }
 }


 compileOptions {
 sourceCompatibility JavaVersion.VERSION_1_8
 targetCompatibility JavaVersion.VERSION_1_8
 }
 kotlinOptions {
 jvmTarget = '1.8'
 }
 buildFeatures {
 compose true
 }
 composeOptions {
 kotlinCompilerExtensionVersion '1.4.3'
 }
 packagingOptions {
 resources {
 excludes += '/META-INF/{AL2.0,LGPL2.1}'
 }
 }

 signingConfigs {
 release {
 storeFile file("/root/android/mlc-llm/android/MLCChat/my-release-key.jks")
 storePassword "123456"
 keyAlias "mykey"
 keyPassword "123456"
 }
 }

 buildTypes {
 release {
 minifyEnabled false
 proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
 signingConfig signingConfigs.release
 }
 }
}

dependencies {
 implementation project(":mlc4j")
 implementation 'androidx.core:core-ktx:1.10.1'
 implementation 'androidx.lifecycle:lifecycle-runtime-ktx:2.6.1'
 implementation 'androidx.activity:activity-compose:1.7.1'
 implementation platform('androidx.compose:compose-bom:2022.10.00')
 implementation 'androidx.lifecycle:lifecycle-viewmodel-compose:2.6.1'
 implementation 'androidx.compose.ui:ui'
 implementation 'androidx.compose.ui:ui-graphics'
 implementation 'androidx.compose.ui:ui-tooling-preview'
 implementation 'androidx.compose.material3:material3:1.1.0'
 implementation 'androidx.compose.material:material-icons-extended'
 implementation 'androidx.appcompat:appcompat:1.6.1'
 implementation 'androidx.navigation:navigation-compose:2.5.3'
 implementation 'com.google.code.gson:gson:2.10.1'
 implementation fileTree(dir: 'src/main/libs', include: ['*.aar', '*.jar'], exclude: [])
 testImplementation 'junit:junit:4.13.2'
 androidTestImplementation 'androidx.test.ext:junit:1.1.5'
 androidTestImplementation 'androidx.test.espresso:espresso-core:3.5.1'
 androidTestImplementation platform('androidx.compose:compose-bom:2022.10.00')
 androidTestImplementation 'androidx.compose.ui:ui-test-junit4'
 debugImplementation 'androidx.compose.ui:ui-tooling'
 debugImplementation 'androidx.compose.ui:ui-test-manifest'

}
```

### 3.5 命令行编译
运行编译命令，完成后在`app/build/outputs/apk/release`生成`app-release.apk`安装包，下载到手机上运行
运行App需要能访问huggingface下载模型(参考文档中的bundle方法需要ADB刷入模型数据)
```
./gradlew assembleRelease
```
![image](https://github.com/user-attachments/assets/df7673f7-7128-4079-b77d-37093fff6660)



### 3.6 运行体验
运行App需要能访问huggingface下载模型

<div align="center">
 <img src="https://github.com/user-attachments/assets/cd9fe502-490e-40c6-8649-30671a4fe504" width="50%" height="50%">
</div>


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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vansin
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Aug 2, 2024
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#1448
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# InternLM 1.8B 安卓端部署实践
![image](https://github.com/user-attachments/assets/5ede99d5-e82b-4ff8-acd8-407f9277967a)

本文将带大家手把手使用[mlc-llm](https://llm.mlc.ai/docs/deploy/android.html#android-sdk)将 InternLM2 部署到安卓手机上

首先我们来看一下最终的效果～
<div align="center">
 <img src="https://github.com/user-attachments/assets/cd9fe502-490e-40c6-8649-30671a4fe504" width="50%" height="50%">
</div>

## 1 环境准备
### 1.1 安装rust 
参考 [https://forge.rust-lang.org/infra/other-installation-methods.html#which](https://forge.rust-lang.org/infra/other-installation-methods.html#which)

使用了国内的镜像，出现选项直接Enter

```
export RUSTUP_DIST_SERVER=https://mirrors.ustc.edu.cn/rust-static
export RUSTUP_UPDATE_ROOT=https://mirrors.ustc.edu.cn/rust-static/rustup
curl --proto '=https' --tlsv1.2 -sSf https://mirrors.ustc.edu.cn/misc/rustup-install.sh | sh
```

### 1.2 安装Android Studio
参考 [https://developer.android.com/studio](https://developer.android.com/studio)

```
mkdir -p /root/android && cd /root/android
wget https://redirector.gvt1.com/edgedl/android/studio/ide-zips/2024.1.1.12/android-studio-2024.1.1.12-linux.tar.gz
tar -xvzf android-studio-2024.1.1.12-linux.tar.gz
cd android-studio
wget https://dl.google.com/android/repository/commandlinetools-linux-11076708_latest.zip?hl=zh-cn
unzip commandlinetools-linux-11076708_latest.zip\?hl\=zh-cn
export JAVA_HOME=/root/Downloads/android-studio/jbr
cmdline-tools/bin/sdkmanager "ndk;27.0.12077973" "cmake;3.22.1" "platforms;android-34" "build-tools;33.0.1" --sdk_root='sdk'
```


### 1.3 设置环境变量
```
. "$HOME/.cargo/env"
export ANDROID_NDK=/root/android/android-studio/sdk/ndk/27.0.12077973
export TVM_NDK_CC=$ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64/bin/aarch64-linux-android24-clang
export JAVA_HOME=/root/android//android-studio/jbr
export ANDROID_HOME=/root/android/android-studio/sdk
export PATH=/usr/local/cuda-12/bin:$PATH
export PATH=/root/android/android-studio/sdk/cmake/3.22.1/bin:$PATH
```
## 2 转换模型
### 2.1 安装mlc-llm
参考[https://llm.mlc.ai/docs/install/mlc_llm.html](https://llm.mlc.ai/docs/install/mlc_llm.html)
（如果下载很慢可以取消重新运行一下，或者本地下载了拷过去）
```
conda create --name mlc-prebuilt python=3.11
conda activate mlc-prebuilt
conda install -c conda-forge git-lfs
pip install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 transformers sentencepiece protobuf
wget https://github.com/mlc-ai/package/releases/download/v0.9.dev0/mlc_llm_nightly_cu122-0.1.dev1445-cp311-cp311-manylinux_2_28_x86_64.whl
wget https://github.com/mlc-ai/package/releases/download/v0.9.dev0/mlc_ai_nightly_cu122-0.15.dev404-cp311-cp311-manylinux_2_28_x86_64.whl
pip install mlc_ai_nightly_cu122-0.15.dev404-cp311-cp311-manylinux_2_28_x86_64.whl
pip install mlc_llm_nightly_cu122-0.1.dev1445-cp311-cp311-manylinux_2_28_x86_64.whl
```
测试如下输出说明安装正确

```
python -c "import mlc_llm; print(mlc_llm)"
```
![image](https://github.com/user-attachments/assets/f497f704-de19-4043-b901-af26856a62c9)

克隆项目
```
git clone https://github.com/mlc-ai/mlc-llm.git
cd mlc-llm
git submodule update --init --recursive
```

### 2.2 转换参数
You can be under the mlc-llm repo, or your own working directory. Note that all platforms can share the same compiled/quantized weights. See [Compile Command Specification](https://llm.mlc.ai/docs/compilation/compile_models.html#compile-command-specification) for specification of `convert_weight`.

```
cd android/MLCChat 
export TVM_SOURCE_DIR=/root/android/mlc-llm/3rdparty/tvm
export MLC_LLM_SOURCE_DIR=/root/android/mlc-llm
mlc_llm convert_weight /root/models/internlm2-chat-1_8b-sft/ \
 --quantization q4f16_1 \
 -o dist/internlm2-chat-1_8b-sft-q4f16_1-MLC
```
### 2.3 生成配置
Use mlc_llm gen_config to generate mlc-chat-config.json and process tokenizers. See [Compile Command Specification](https://llm.mlc.ai/docs/compilation/compile_models.html#compile-command-specification) for specification of `gen_config`.

```

mlc_llm gen_config /root/models/internlm2-chat-1_8b-sft/ \
 --quantization q4f16_1 --conv-template chatml \
 -o dist/internlm2-chat-1_8b-sft-q4f16_1-MLC

```
### 2.4 上传到huggingface
上传这一步需要能访问huggingface，可能需要部署代理
如果没有代理可以直接在接下来的配置中使用如下链接的模型（和文档中的转换方法一样）
[https://huggingface.co/timws/internlm2-chat-1_8b-sft-q4f16_1-MLC](https://huggingface.co/timws/internlm2-chat-1_8b-sft-q4f16_1-MLC)
### 2.5 (可选) 测试转换的模型
在打包之前可以测试模型效果，需要编译成二进制文件
在个人电脑上运行测试代码正常，**InternStudio**上**暂未成功**
```

mlc_llm compile ./dist/internlm2-chat-1_8b-sft-q4f16_1-MLC/mlc-chat-config.json \
 --device cuda -o dist/libs/internlm2-chat-1_8b-sft-q4f16_1-MLC-cuda.so
```
测试编译的模型是否符合预期，手机端运行的效果和测试效果接近
```python3
from mlc_llm import MLCEngine

# Create engine
engine = MLCEngine(model="./dist/internlm2-1_8b-q4f16_1-MLC", model_lib="./dist/libs/internlm2-1_8b-q4f16_1-MLC-cuda.so")

# Run chat completion in OpenAI API.
print(engine)
for response in engine.chat.completions.create(
 messages=[{"role": "user", "content": "你是谁？"}],
 stream=True
):
 for choice in response.choices:
 print(choice.delta.content, end="", flush=True)
print("\n")
engine.terminate()
```

## 3 打包运行
### 3.1 修改配置文件
修改`mlc-package-config.json`
参考如下
```
{
 "device": "android",
 "model_list": [
 {
 "model": "HF://timws/internlm2-chat-1_8b-sft-q4f16_1-MLC",
 "estimated_vram_bytes": 3980990464,
 "model_id": "internlm2-chat-1_8b-sft-q4f16_1-MLC"

 },
 {
 "model": "HF://mlc-ai/gemma-2b-it-q4f16_1-MLC",
 "model_id": "gemma-2b-q4f16_1-MLC",
 "estimated_vram_bytes": 3980990464
 }
 ]
}

```

### 3.2 运行打包命令

```
 mlc_llm package
```
![image](https://github.com/user-attachments/assets/0e1db9c5-6252-4c6f-81b2-a739fa4fac44)


### 3.3 创建签名
```
cd /root/android/mlc-llm/android/MLCChat
/root/android/android-studio/jbr/bin/keytool -genkey -v -keystore my-release-key.jks -keyalg RSA -keysize 2048 -validity 10000
Enter keystore password: 
Re-enter new password: 
What is your first and last name?
 [Unknown]: Any
What is the name of your organizational unit?
 [Unknown]: Any
What is the name of your organization?
 [Unknown]: Any
What is the name of your City or Locality?
 [Unknown]: Any
What is the name of your State or Province?
 [Unknown]: Any
What is the two-letter country code for this unit?
 [Unknown]: CN
Is CN=Any, OU=Any, O=Any, L=Any, ST=Any, C=CN correct?
 [no]: yes

Generating 2,048 bit RSA key pair and self-signed certificate (SHA256withRSA) with a validity of 10,000 days
 for: CN=Any, OU=Any, O=Any, L=Any, ST=Any, C=CN
[Storing my-release-key.jks]
```
### 3.4 修改gradle配置
如果是本地可以WIFI或USB调试不用签名，在服务器构建需要签名
修改`app/build.gradle`为如下内容，主要是增加了签名部分，注意确认签名文件的位置
```
plugins {
 id 'com.android.application'
 id 'org.jetbrains.kotlin.android'
}

android {
 namespace 'ai.mlc.mlcchat'
 compileSdk 34

 defaultConfig {
 applicationId "ai.mlc.mlcchat"
 minSdk 26
 targetSdk 33
 versionCode 1
 versionName "1.0"

 testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
 vectorDrawables {
 useSupportLibrary true
 }
 }


 compileOptions {
 sourceCompatibility JavaVersion.VERSION_1_8
 targetCompatibility JavaVersion.VERSION_1_8
 }
 kotlinOptions {
 jvmTarget = '1.8'
 }
 buildFeatures {
 compose true
 }
 composeOptions {
 kotlinCompilerExtensionVersion '1.4.3'
 }
 packagingOptions {
 resources {
 excludes += '/META-INF/{AL2.0,LGPL2.1}'
 }
 }

 signingConfigs {
 release {
 storeFile file("/root/android/mlc-llm/android/MLCChat/my-release-key.jks")
 storePassword "123456"
 keyAlias "mykey"
 keyPassword "123456"
 }
 }

 buildTypes {
 release {
 minifyEnabled false
 proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
 signingConfig signingConfigs.release
 }
 }
}

dependencies {
 implementation project(":mlc4j")
 implementation 'androidx.core:core-ktx:1.10.1'
 implementation 'androidx.lifecycle:lifecycle-runtime-ktx:2.6.1'
 implementation 'androidx.activity:activity-compose:1.7.1'
 implementation platform('androidx.compose:compose-bom:2022.10.00')
 implementation 'androidx.lifecycle:lifecycle-viewmodel-compose:2.6.1'
 implementation 'androidx.compose.ui:ui'
 implementation 'androidx.compose.ui:ui-graphics'
 implementation 'androidx.compose.ui:ui-tooling-preview'
 implementation 'androidx.compose.material3:material3:1.1.0'
 implementation 'androidx.compose.material:material-icons-extended'
 implementation 'androidx.appcompat:appcompat:1.6.1'
 implementation 'androidx.navigation:navigation-compose:2.5.3'
 implementation 'com.google.code.gson:gson:2.10.1'
 implementation fileTree(dir: 'src/main/libs', include: ['*.aar', '*.jar'], exclude: [])
 testImplementation 'junit:junit:4.13.2'
 androidTestImplementation 'androidx.test.ext:junit:1.1.5'
 androidTestImplementation 'androidx.test.espresso:espresso-core:3.5.1'
 androidTestImplementation platform('androidx.compose:compose-bom:2022.10.00')
 androidTestImplementation 'androidx.compose.ui:ui-test-junit4'
 debugImplementation 'androidx.compose.ui:ui-tooling'
 debugImplementation 'androidx.compose.ui:ui-test-manifest'

}
```

### 3.5 命令行编译
运行编译命令，完成后在`app/build/outputs/apk/release`生成`app-release.apk`安装包，下载到手机上运行
运行App需要能访问huggingface下载模型(参考文档中的bundle方法需要ADB刷入模型数据)
```
./gradlew assembleRelease
```
![image](https://github.com/user-attachments/assets/df7673f7-7128-4079-b77d-37093fff6660)



### 3.6 运行体验
运行App需要能访问huggingface下载模型

<div align="center">
 <img src="https://github.com/user-attachments/assets/cd9fe502-490e-40c6-8649-30671a4fe504" width="50%" height="50%">
</div>


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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 Merged

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# InternLM 1.8B 安卓端部署实践
![image](https://github.com/user-attachments/assets/5ede99d5-e82b-4ff8-acd8-407f9277967a)

本文将带大家手把手使用[mlc-llm](https://llm.mlc.ai/docs/deploy/android.html#android-sdk)将 InternLM2 部署到安卓手机上

首先我们来看一下最终的效果～
<div align="center">
 <img src="https://github.com/user-attachments/assets/cd9fe502-490e-40c6-8649-30671a4fe504" width="50%" height="50%">
</div>

## 1 环境准备
### 1.1 安装rust 
参考 [https://forge.rust-lang.org/infra/other-installation-methods.html#which](https://forge.rust-lang.org/infra/other-installation-methods.html#which)

使用了国内的镜像，出现选项直接Enter

```
export RUSTUP_DIST_SERVER=https://mirrors.ustc.edu.cn/rust-static
export RUSTUP_UPDATE_ROOT=https://mirrors.ustc.edu.cn/rust-static/rustup
curl --proto '=https' --tlsv1.2 -sSf https://mirrors.ustc.edu.cn/misc/rustup-install.sh | sh
```

### 1.2 安装Android Studio
参考 [https://developer.android.com/studio](https://developer.android.com/studio)

```
mkdir -p /root/android && cd /root/android
wget https://redirector.gvt1.com/edgedl/android/studio/ide-zips/2024.1.1.12/android-studio-2024.1.1.12-linux.tar.gz
tar -xvzf android-studio-2024.1.1.12-linux.tar.gz
cd android-studio
wget https://dl.google.com/android/repository/commandlinetools-linux-11076708_latest.zip?hl=zh-cn
unzip commandlinetools-linux-11076708_latest.zip\?hl\=zh-cn
export JAVA_HOME=/root/Downloads/android-studio/jbr
cmdline-tools/bin/sdkmanager "ndk;27.0.12077973" "cmake;3.22.1" "platforms;android-34" "build-tools;33.0.1" --sdk_root='sdk'
```


### 1.3 设置环境变量
```
. "$HOME/.cargo/env"
export ANDROID_NDK=/root/android/android-studio/sdk/ndk/27.0.12077973
export TVM_NDK_CC=$ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64/bin/aarch64-linux-android24-clang
export JAVA_HOME=/root/android//android-studio/jbr
export ANDROID_HOME=/root/android/android-studio/sdk
export PATH=/usr/local/cuda-12/bin:$PATH
export PATH=/root/android/android-studio/sdk/cmake/3.22.1/bin:$PATH
```
## 2 转换模型
### 2.1 安装mlc-llm
参考[https://llm.mlc.ai/docs/install/mlc_llm.html](https://llm.mlc.ai/docs/install/mlc_llm.html)
（如果下载很慢可以取消重新运行一下，或者本地下载了拷过去）
```
conda create --name mlc-prebuilt python=3.11
conda activate mlc-prebuilt
conda install -c conda-forge git-lfs
pip install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 transformers sentencepiece protobuf
wget https://github.com/mlc-ai/package/releases/download/v0.9.dev0/mlc_llm_nightly_cu122-0.1.dev1445-cp311-cp311-manylinux_2_28_x86_64.whl
wget https://github.com/mlc-ai/package/releases/download/v0.9.dev0/mlc_ai_nightly_cu122-0.15.dev404-cp311-cp311-manylinux_2_28_x86_64.whl
pip install mlc_ai_nightly_cu122-0.15.dev404-cp311-cp311-manylinux_2_28_x86_64.whl
pip install mlc_llm_nightly_cu122-0.1.dev1445-cp311-cp311-manylinux_2_28_x86_64.whl
```
测试如下输出说明安装正确

```
python -c "import mlc_llm; print(mlc_llm)"
```
![image](https://github.com/user-attachments/assets/f497f704-de19-4043-b901-af26856a62c9)

克隆项目
```
git clone https://github.com/mlc-ai/mlc-llm.git
cd mlc-llm
git submodule update --init --recursive
```

### 2.2 转换参数
You can be under the mlc-llm repo, or your own working directory. Note that all platforms can share the same compiled/quantized weights. See [Compile Command Specification](https://llm.mlc.ai/docs/compilation/compile_models.html#compile-command-specification) for specification of `convert_weight`.

```
cd android/MLCChat 
export TVM_SOURCE_DIR=/root/android/mlc-llm/3rdparty/tvm
export MLC_LLM_SOURCE_DIR=/root/android/mlc-llm
mlc_llm convert_weight /root/models/internlm2-chat-1_8b-sft/ \
 --quantization q4f16_1 \
 -o dist/internlm2-chat-1_8b-sft-q4f16_1-MLC
```
### 2.3 生成配置
Use mlc_llm gen_config to generate mlc-chat-config.json and process tokenizers. See [Compile Command Specification](https://llm.mlc.ai/docs/compilation/compile_models.html#compile-command-specification) for specification of `gen_config`.

```

mlc_llm gen_config /root/models/internlm2-chat-1_8b-sft/ \
 --quantization q4f16_1 --conv-template chatml \
 -o dist/internlm2-chat-1_8b-sft-q4f16_1-MLC

```
### 2.4 上传到huggingface
上传这一步需要能访问huggingface，可能需要部署代理
如果没有代理可以直接在接下来的配置中使用如下链接的模型（和文档中的转换方法一样）
[https://huggingface.co/timws/internlm2-chat-1_8b-sft-q4f16_1-MLC](https://huggingface.co/timws/internlm2-chat-1_8b-sft-q4f16_1-MLC)
### 2.5 (可选) 测试转换的模型
在打包之前可以测试模型效果，需要编译成二进制文件
在个人电脑上运行测试代码正常，**InternStudio**上**暂未成功**
```

mlc_llm compile ./dist/internlm2-chat-1_8b-sft-q4f16_1-MLC/mlc-chat-config.json \
 --device cuda -o dist/libs/internlm2-chat-1_8b-sft-q4f16_1-MLC-cuda.so
```
测试编译的模型是否符合预期，手机端运行的效果和测试效果接近
```python3
from mlc_llm import MLCEngine

# Create engine
engine = MLCEngine(model="./dist/internlm2-1_8b-q4f16_1-MLC", model_lib="./dist/libs/internlm2-1_8b-q4f16_1-MLC-cuda.so")

# Run chat completion in OpenAI API.
print(engine)
for response in engine.chat.completions.create(
 messages=[{"role": "user", "content": "你是谁？"}],
 stream=True
):
 for choice in response.choices:
 print(choice.delta.content, end="", flush=True)
print("\n")
engine.terminate()
```

## 3 打包运行
### 3.1 修改配置文件
修改`mlc-package-config.json`
参考如下
```
{
 "device": "android",
 "model_list": [
 {
 "model": "HF://timws/internlm2-chat-1_8b-sft-q4f16_1-MLC",
 "estimated_vram_bytes": 3980990464,
 "model_id": "internlm2-chat-1_8b-sft-q4f16_1-MLC"

 },
 {
 "model": "HF://mlc-ai/gemma-2b-it-q4f16_1-MLC",
 "model_id": "gemma-2b-q4f16_1-MLC",
 "estimated_vram_bytes": 3980990464
 }
 ]
}

```

### 3.2 运行打包命令

```
 mlc_llm package
```
![image](https://github.com/user-attachments/assets/0e1db9c5-6252-4c6f-81b2-a739fa4fac44)


### 3.3 创建签名
```
cd /root/android/mlc-llm/android/MLCChat
/root/android/android-studio/jbr/bin/keytool -genkey -v -keystore my-release-key.jks -keyalg RSA -keysize 2048 -validity 10000
Enter keystore password: 
Re-enter new password: 
What is your first and last name?
 [Unknown]: Any
What is the name of your organizational unit?
 [Unknown]: Any
What is the name of your organization?
 [Unknown]: Any
What is the name of your City or Locality?
 [Unknown]: Any
What is the name of your State or Province?
 [Unknown]: Any
What is the two-letter country code for this unit?
 [Unknown]: CN
Is CN=Any, OU=Any, O=Any, L=Any, ST=Any, C=CN correct?
 [no]: yes

Generating 2,048 bit RSA key pair and self-signed certificate (SHA256withRSA) with a validity of 10,000 days
 for: CN=Any, OU=Any, O=Any, L=Any, ST=Any, C=CN
[Storing my-release-key.jks]
```
### 3.4 修改gradle配置
如果是本地可以WIFI或USB调试不用签名，在服务器构建需要签名
修改`app/build.gradle`为如下内容，主要是增加了签名部分，注意确认签名文件的位置
```
plugins {
 id 'com.android.application'
 id 'org.jetbrains.kotlin.android'
}

android {
 namespace 'ai.mlc.mlcchat'
 compileSdk 34

 defaultConfig {
 applicationId "ai.mlc.mlcchat"
 minSdk 26
 targetSdk 33
 versionCode 1
 versionName "1.0"

 testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
 vectorDrawables {
 useSupportLibrary true
 }
 }


 compileOptions {
 sourceCompatibility JavaVersion.VERSION_1_8
 targetCompatibility JavaVersion.VERSION_1_8
 }
 kotlinOptions {
 jvmTarget = '1.8'
 }
 buildFeatures {
 compose true
 }
 composeOptions {
 kotlinCompilerExtensionVersion '1.4.3'
 }
 packagingOptions {
 resources {
 excludes += '/META-INF/{AL2.0,LGPL2.1}'
 }
 }

 signingConfigs {
 release {
 storeFile file("/root/android/mlc-llm/android/MLCChat/my-release-key.jks")
 storePassword "123456"
 keyAlias "mykey"
 keyPassword "123456"
 }
 }

 buildTypes {
 release {
 minifyEnabled false
 proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
 signingConfig signingConfigs.release
 }
 }
}

dependencies {
 implementation project(":mlc4j")
 implementation 'androidx.core:core-ktx:1.10.1'
 implementation 'androidx.lifecycle:lifecycle-runtime-ktx:2.6.1'
 implementation 'androidx.activity:activity-compose:1.7.1'
 implementation platform('androidx.compose:compose-bom:2022.10.00')
 implementation 'androidx.lifecycle:lifecycle-viewmodel-compose:2.6.1'
 implementation 'androidx.compose.ui:ui'
 implementation 'androidx.compose.ui:ui-graphics'
 implementation 'androidx.compose.ui:ui-tooling-preview'
 implementation 'androidx.compose.material3:material3:1.1.0'
 implementation 'androidx.compose.material:material-icons-extended'
 implementation 'androidx.appcompat:appcompat:1.6.1'
 implementation 'androidx.navigation:navigation-compose:2.5.3'
 implementation 'com.google.code.gson:gson:2.10.1'
 implementation fileTree(dir: 'src/main/libs', include: ['*.aar', '*.jar'], exclude: [])
 testImplementation 'junit:junit:4.13.2'
 androidTestImplementation 'androidx.test.ext:junit:1.1.5'
 androidTestImplementation 'androidx.test.espresso:espresso-core:3.5.1'
 androidTestImplementation platform('androidx.compose:compose-bom:2022.10.00')
 androidTestImplementation 'androidx.compose.ui:ui-test-junit4'
 debugImplementation 'androidx.compose.ui:ui-tooling'
 debugImplementation 'androidx.compose.ui:ui-test-manifest'

}
```

### 3.5 命令行编译
运行编译命令，完成后在`app/build/outputs/apk/release`生成`app-release.apk`安装包，下载到手机上运行
运行App需要能访问huggingface下载模型(参考文档中的bundle方法需要ADB刷入模型数据)
```
./gradlew assembleRelease
```
![image](https://github.com/user-attachments/assets/df7673f7-7128-4079-b77d-37093fff6660)



### 3.6 运行体验
运行App需要能访问huggingface下载模型

<div align="center">
 <img src="https://github.com/user-attachments/assets/cd9fe502-490e-40c6-8649-30671a4fe504" width="50%" height="50%">
</div>