Summary of Read-me: Refactorizing Llms As Router-decoupled Mixture Of Experts with System Co-design, by Ruisi Cai et al.
Read-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design
by Ruisi Cai, Yeonju Ro, Geon-Woo Kim, Peihao Wang, Babak Ehteshami Bejnordi, Aditya Akella, Zhangyang Wang
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 proposes a novel framework called Read-ME that transforms pre-trained large language models (LLMs) into smaller Mixture-of-Experts (MoE) models, addressing the challenges of inefficient memory management and suboptimal batching during inference. The approach employs activation sparsity to extract experts and introduces a pre-gating router decoupled from the MoE backbone for system-friendly pre-computing and lookahead scheduling. This codesign addresses critical gaps on both algorithmic and system fronts, providing a scalable and efficient alternative for LLM inference in resource-constrained settings. The proposed method outperforms other popular open-source dense models of similar scales, achieving improvements of up to 10.1% on MMLU, and improving mean end-to-end latency up to 6.1%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to make big language models smaller and faster. It uses a special technique called Read-ME that makes it easier to use these models on devices with limited memory. This is important because many people want to use these models on their phones or tablets, but they can’t because the models are too big. The new method works by finding the most important parts of the model and using them to make smaller versions. It also helps reduce the time it takes to process information. The results show that this method is much better than other methods, making it a great tool for anyone who wants to use language models on devices with limited resources. |
Keywords
» Artificial intelligence » Inference » Mixture of experts
