Summary of Mobileaibench: Benchmarking Llms and Lmms For On-device Use Cases, by Rithesh Murthy et al.
MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases
by Rithesh Murthy, Liangwei Yang, Juntao Tan, Tulika Manoj Awalgaonkar, Yilun Zhou, Shelby Heinecke, Sachin Desai, Jason Wu, Ran Xu, Sarah Tan, Jianguo Zhang, Zhiwei Liu, Shirley Kokane, Zuxin Liu, Ming Zhu, Huan Wang, Caiming Xiong, Silvio Savarese
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the challenges in deploying Large Language Models (LLMs) and Large Multimodal Models (LMMs) on mobile devices, focusing on enhancing privacy, stability, and personalization. To overcome hardware constraints, the authors explore model compression techniques like quantization, analyzing its impact on various task performances, including LLM tasks, LMM tasks, and trust/safety aspects. The paper introduces MobileAIBench, a comprehensive benchmarking framework for evaluating mobile-optimized LLMs and LMMs. This framework assesses models across different sizes, quantization levels, and tasks, measuring latency and resource consumption on real devices. The authors also provide insights into the performance and feasibility of deploying LLMs and LMMs on mobile platforms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how to use powerful computer models called Large Language Models (LLMs) and Large Multimodal Models (LMMs) on smartphones. This is important because it helps keep personal data safe, makes the phone more stable, and allows for personalized experiences. The authors look into ways to make these models smaller and faster so they can run smoothly on phones. They also create a special tool called MobileAIBench that helps test these models on real devices to see how well they work. This research is important because it will help people deploy these powerful models on their phones, which can have many benefits. |
Keywords
» Artificial intelligence » Model compression » Quantization
