# Build a customer support chatbot on Android with Llama and ExecuTorch

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/)
- [Create a development environment](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/how-to-1/)
- [Set up ExecuTorch](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/how-to-2/)
- [Understand Llama models](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/how-to-3/)
- [Prepare Llama models for ExecuTorch](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/how-to-4/)
- [Run the chatbot on Android](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/how-to-5/)
- [Build and run the Android chat app](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/how-to-6/)
- [Next Steps](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_next-steps/)

## About this Learning Path

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| Skill level: | Introductory |
| Reading time: | 1 hr |
| Last updated: | 05 Aug 2026 |

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| Author: | Parichay Das<br>[GitHub](https://github.com/parichaydas)<br>[LinkedIn](https://linkedin.com/in/parichaydas) |
| Arm IP: | [Cortex-A](https://support.arm.com/?tab=compute-ip&Product%20Type=Application%20Processors) |
| Tags: | [ML](https://learn.arm.com/tag/ml) [macOS](https://learn.arm.com/tag/macos) [Linux](https://learn.arm.com/tag/linux) [Android](https://learn.arm.com/tag/android) [Java](https://learn.arm.com/tag/java) [Python](https://learn.arm.com/tag/python) [ExecuTorch](https://learn.arm.com/tag/executorch) |

## Who is this for?

This is an introductory topic for software developers interested in building an on-device customer support chatbot for Android using Meta's Llama models and the ExecuTorch runtime.

## What will you learn?

After completing this Learning Path, you'll be able to perform the following tasks:

- Set up a development environment for building and deploying ExecuTorch-based apps on Android
- Describe how ExecuTorch uses KleidiAI kernels to accelerate performance on Arm-based platforms
- Export a Llama 3.2 model to .pte format optimized for on-device inference
- Run a Llama model on an Arm-powered Android phone and verify inference performance
- Build and run an Android chat app configured as a customer support assistant

## Prerequisites

Before starting, you'll need the following prerequisites:

- An Apple M1/M2/M3 development machine, or a Linux machine with at least 16GB of RAM
- An Arm-powered smartphone with the i8mm feature running Android, with 16GB of RAM
- A USB cable to connect your smartphone to your development machine
- Android Debug Bridge (adb) installed. Follow the steps in [adb](https://developer.android.com/tools/adb) to install Android SDK Platform Tools
- Java 17 JDK. Follow the steps in [Java SE 17 Archive Downloads](https://www.oracle.com/java/technologies/javase/jdk17-archive-downloads.html) to download and install JDK for your host
- Python 3.10 or later
- A [Hugging Face](https://huggingface.co/) account with access to Meta Llama models

## Summary

You’ll build and deploy an on-device Android customer support chatbot with Meta’s Llama 3.2, ExecuTorch, and KleidiAI. First, you’ll set up the environment and choose a model and quantization approach. Then, you’ll obtain Llama 3.2 1B Instruct weights and export them to `.pte`. Finally, you’ll cross-compile the runner with the Android NDK and enable KleidiAI through XNNPACK, then deploy to an Arm phone and run inference.

## Frequently asked questions

**Which Llama model variant should I download, and can I use a different one?**

You should use the Llama 3.2 1B Instruct model. If you want to use a different model, you can adapt the same steps to other variants with minimal modification.

**What output should I have after exporting the model for ExecuTorch?**

You should produce a `.pte` file. This format is optimized for on-device inference and is loadable by the ExecuTorch runtime.

**What should I check if the Android cross-compile step fails to find the toolchain?**

Verify that `ANDROID_NDK` is set to the correct path and that `build/cmake/android.toolchain.cmake` exists there. Also confirm your ExecuTorch dependencies are installed in the Python environment.

**How do I make sure KleidiAI acceleration is included in my Android build?**

Build ExecuTorch and its libraries for Android with KleidiAI kernels enabled through XNNPACK. Use the provided CMake configuration to include these kernels.

**What result should I expect when I run the chatbot on the phone?**

The Llama runner or chat app should load the exported model and generate responses to prompts on the device.