Summary of Puzzle: Distillation-based Nas For Inference-optimized Llms, by Akhiad Bercovich et al.

Puzzle: Distillation-Based NAS for Inference-Optimized LLMs

by Akhiad Bercovich, Tomer Ronen, Talor Abramovich, Nir Ailon, Nave Assaf, Mohammad Dabbah, Ido Galil, Amnon Geifman, Yonatan Geifman, Izhak Golan, Netanel Haber, Ehud Karpas, Roi Koren, Itay Levy, Pavlo Molchanov, Shahar Mor, Zach Moshe, Najeeb Nabwani, Omri Puny, Ran Rubin, Itamar Schen, Ido Shahaf, Oren Tropp, Omer Ullman Argov, Ran Zilberstein, Ran El-Yaniv

First submitted to arxiv on: 28 Nov 2024

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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 proposed Puzzle framework accelerates large language model inference on specific hardware, narrowing the gap between state-of-the-art capabilities and practical deployability. By applying neural architecture search at an unprecedented scale, Puzzle optimizes models with tens of billions of parameters under hardware constraints. The approach utilizes blockwise local knowledge distillation for parallel architecture exploration and mixed-integer programming for precise constraint optimization. This innovation has the potential to enhance the adoption of large language models in real-world applications.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Puzzle is a new way to make big language models work faster on special computers. Right now, these models are very good at doing tasks like answering questions or generating text, but they use too many resources and can’t be used easily. The Puzzle team found a way to make these models work better on specific computers by using a technique called neural architecture search. This method helps the model find the best way to work on the computer without wasting time or energy. With Puzzle, we might be able to use big language models in more everyday situations.