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The Method Google Used to Reduce LLM Size by 66%

Computer ScienceMachine Learning

About this opportunity

Google recently released a technical report on Gemma 2, the next generation of their open Large Language Model (LLM). This report provides a comprehensive case study on the use of knowledge distillation for training LLMs. The method, which trains a smaller model to mimic the outputs of a larger one, allowed Google to reduce the size of their Gemma 2 model from 27B parameters to 9B parameters while maintaining 96% user satisfaction. Key highlights: - Knowledge distillation can reduce model size by up to 70% with only a 3-10% performance loss. - Distilled models can outperform same-sized models trained from scratch. - Google’s Gemma 2 report shows distilled models have lower perplexity scores and better user satisfaction. Knowledge distillation presents numerous benefits such as reduced training costs, faster inference times, improved accessibility, and a lower carbon footprint. However, it also comes with drawbacks, including the need for a large teacher model and potential legal ramifications when using proprietary models.

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