Summary of Amoeballm: Constructing Any-shape Large Language Models For Efficient and Instant Deployment, by Yonggan Fu et al.
AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment
by Yonggan Fu, Zhongzhi Yu, Junwei Li, Jiayi Qian, Yongan Zhang, Xiangchi Yuan, Dachuan Shi, Roman Yakunin, Yingyan Celine Lin
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel framework called AmoebaLLM is proposed to efficiently deploy large language models (LLMs) across diverse real-world applications and platforms. The framework enables the instant derivation of LLM subnets with arbitrary shapes, achieving optimal accuracy-efficiency frontiers and allowing for rapid deployment tailored to various platforms and applications. This is achieved through three innovative components: a knowledge-preserving subnet selection strategy, a shape-aware mixture of LoRAs, and an in-place distillation scheme. Extensive experiments validate that AmoebaLLM sets new standards in LLM adaptability and delivers subnets with state-of-the-art trade-offs between accuracy and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AmoebaLLM is a new way to use large language models (LLMs) in different places and situations. Currently, these powerful tools are hard to use because they need to be adjusted for each specific task or platform. AmoebaLLM makes it possible to quickly create smaller versions of LLMs that work well on different devices or for different tasks. This is done using three special techniques: a way to choose the right parts of the model, a way to combine those parts smoothly, and a way to fine-tune the model so it works well. |
Keywords
» Artificial intelligence » Distillation
