---

MobileAIBench

A comprehensive benchmark designed to evaluate the performance and resource consumptions of LLMs & LMMs for on-device use cases.

Installation

To install MobileBench, follow these steps:

1. Clone the Repository:

   git clone --recurse-submodules https://github.com/SalesforceAIResearch/MobileAIBench.git

2. Create a Conda Environment:

   conda create -n mobile_bench python=3.10
   conda activate mobile_bench

3. Run the Makefile:

   make

4. Add OpenAI API Key:

   export OPENAI_API_KEY=<OPENAI_API_KEY>

Usage

Here are some usage examples for running MobileAIBench:

Task: Question Answering

• Dataset: hotpot_qa & databricks-15k

• Model: xgen2-3b.gguf

• Run on GPU:

  python ./src/mobile_bench.py --task question_answering --model_lib llama_cpp_python --model_name xgen2-3b.gguf --use_gpu

• Run on CPU:

  python ./src/mobile_bench.py --task question_answering --model_lib llama_cpp_python --model_name xgen2-3b.gguf

Task: All (Standard_NLP and Trust & Safety)

• Model: xgen2-3b.gguf

• Run on GPU:

  python ./src/mobile_bench.py --task all --model_lib llama_cpp_python --model_name xgen2-3b.gguf --use_gpu

• Run on CPU:

  python ./src/mobile_bench.py --task all --model_lib llama_cpp_python --model_name xgen2-3b.gguf

Running Mobile App

• To run ios mobile app, refer to ./ios-app/README.md
• Here's a screenshot taken from the ios-app

- To run android mobile app, refer to ./android-app/README.md - Here's a screenshot taken from the android-app