Introduction to MLC-LLM for Android

We are excited to share that we have enabled the Android support for MLC-LLM. Checkout the instruction page for instructions to download and install our Android app. Checkout the announcing blog post for the technical details throughout our process of making MLC-LLM possible for Android.

App Build Instructions

Install TVM Unity. We have some local changes to TVM Unity, so please try out the mlc/relax repo for now. We will migrate change back to TVM Unity soon.
```
git clone https://github.com/mlc-ai/relax.git --recursive
cd relax
mkdir build
cp cmake/config.cmake build
```
in build/config.cmake, set USE_OPENCL and USE_LLVM as ON
```
make -j
export TVM_HOME=$(pwd)
export PYTHONPATH=$PYTHONPATH:$TVM_HOME/python
```
Install Apache Maven for our Java dependency management. Run command mnv --version to verify that Maven is correctly installed.

Build TVM4j (Java Frontend for TVM Runtime).

cd ${TVM_HOME}/jvm; mvn install -pl core -DskipTests -Dcheckstyle.skip=true

Get Model Weight.

Currently we support LLaMA and Vicuna.
1. Get the original LLaMA weights in the HuggingFace format by following the instructions here.
2. Use instructions here to get vicuna weights.
3. Create a soft link to the model path under dist/models.
```
mkdir -p dist/models
ln -s your_model_path dist/models/model_name
# For example:
# ln -s path/to/vicuna-v1-7b dist/models/vicuna-v1-7b
```

Build model to library.

git clone https://github.com/mlc-ai/mlc-llm.git --recursive
cd mlc-llm
python3 build.py --model vicuna-v1-7b --dtype float16 --target android --quantization-mode int4 --quantization-sym --quantization-storage-nbit 32 --max-seq-len 768

Build libraries for Android app.
```
cd android
./prepare_libs.sh
```
Download Android Studio, and use Android Studio to open folder android/MLCChat as the project.
1. Install Android SDK and NDK either inside Android Studio (recommended) or separately.
2. Specify the Android SDK and NDK path in file android/MLCChat/local.properties (if it does not exist, create one):
```
sdk.dir=/path/to/android/sdk
ndk.dir=/path/to/android/ndk
```
  For example, a good local.properties can be:
```
sdk.dir=/Users/me/Library/Android/sdk
ndk.dir=/Users/me/Library/Android/sdk/ndk/25.2.9519653
```
Connect your Android device to your machine. In the menu bar of Android Studio, click Build - Make Project.
Once the build is finished, click Run - Run 'app', and you will see the app launched on your phone.

Use Your Own Model Weights

By following the instructions above, the installed app will download weights from our pre-uploaded HuggingFace repository. If you do not want to download the weights from Internet and instead wish to use the weights you build, please follow the steps below.

Step 1 - step 8: same as section ”App Build Instructions”.
Step 9. In Build - Generate Signed Bundle / APK, build the project to an APK for release. If it is the first time you generate an APK, you will need to create a key. Please follow the official guide from Android for more instructions on this. After generating the release APK, you will get the APK file app-release.apk under android/MLCChat/app/release/.
Step 10. Enable “USB debugging” in the developer options your phone settings.
Step 11. Install Android SDK Platform-Tools for ADB (Android Debug Bridge). The platform tools will be already available under your Android SDK path if you have installed SDK (e.g., at /path/to/android-sdk/platform-tools/). Add the platform-tool path to your PATH environment. Run adb devices to verify that ADB is installed correctly your phone is listed as a device.
Step 12. In command line, run
```
adb install android/MLCChat/app/release/app-release.apk
```
to install the APK to your phone. If it errors with message adb: failed to install android/MLCChat/app/release/app-release.apk: Failure [INSTALL_FAILED_UPDATE_INCOMPATIBLE: Existing package ai.mlc.mlcchat signatures do not match newer version; ignoring!], please uninstall the existing app and try adb install again.

Step 13. Push the tokenizer and model weights to your phone through ADB.

adb push dist/models/vicuna-v1-7b/tokenizer.model /data/local/tmp/vicuna-v1-7b/tokenizer.model
adb push dist/vicuna-v1-7b/float16/params /data/local/tmp/vicuna-v1-7b/params
adb shell "mkdir -p /storage/emulated/0/Android/data/ai.mlc.mlcchat/files/Download/"
adb shell "mv /data/local/tmp/vicuna-v1-7b /storage/emulated/0/Android/data/ai.mlc.mlcchat/files/Download/vicuna-v1-7b"

Step 14. Everything is ready. Launch the MLCChat on your phone and you will be able to use the app with your own weights. You will find that no weight download is needed.