Build a customer support chatbot on Android with Llama and ExecuTorch
Introduction
Create a development environment
Set up ExecuTorch
Understand Llama models
Prepare Llama models for ExecuTorch
Run the chatbot on Android
Build and run the Android chat app
Next Steps
Build a customer support chatbot on Android with Llama and ExecuTorch
Set up ExecuTorch
ExecuTorch is an end-to-end solution for enabling on-device inference across mobile and edge devices, including wearables, embedded devices, and microcontrollers. It is part of the PyTorch Edge ecosystem and enables efficient deployment of PyTorch models to edge devices. You can learn more by reading the ExecuTorch Overview .
The best practice is to create an isolated Python environment to install ExecuTorch dependencies.
Create a Python virtual environment
Use the venv module available through Python:
python3.10 -m venv executorch-venv
source executorch-venv/bin/activate
Your terminal prompt now shows executorch-venv as a prefix to indicate the virtual environment is active.
Clone ExecuTorch and install dependencies
From within the virtual environment, run the commands below to download the ExecuTorch repository and install the required packages:
git clone https://github.com/pytorch/executorch.git
cd executorch
git checkout release/1.0
git submodule sync
git submodule update --init --recursive
./install_executorch.sh
./examples/models/llama/install_requirements.sh
What you’ve learned and what’s next
You have successfully:
- Created an isolated Python environment for ExecuTorch
- Downloaded and configured the ExecuTorch repository
- Installed all required dependencies for Llama model deployment
Before preparing your Llama model for deployment, the next section explains what Llama models are and why they work well for customer support applications.