<ul><li>The application of language models on mobile devices is facing a challenge in terms of computation and memory costs.</li><li>A study has analyzed the effect of various components on tiny language models with 1B parameters.</li><li>Optimization strategies such as tokenizer compression, architecture tweaking, and parameter inheritance have shown effective results.</li><li>Experimental results demonstrate improved optimization and architecture of tiny language models, yielding notable performance improvements.</li></ul>

Rethinking Optimization and Architecture for Tiny Language Models

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