1.4 Bigbooster - Lite

BigBooster closes the gap with 2.7B models while running at nearly the same speed as the base 1.4B model.

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If you are using "Lite" extraction tools or content parsers on your website, updating to 1.4 will noticeably speed up your page rendering times. BigBooster closes the gap with 2

Large language models (LLMs) excel in quality but remain impractical for edge deployment. Sub-2B parameter models (e.g., MobileLLM, Lite-1.4B) offer efficiency but suffer from factual recall and reasoning gaps. The addresses this by integrating a conditional, sparse activation module that boosts representational power without full parameter replication. What is Lite 1

The growing demand for on-device language models requires architectures that balance latency, memory, and output quality. This paper introduces , a 1.4 billion parameter decoder-only model augmented with a lightweight "BigBooster" side-network. The BigBooster module selectively applies high-capacity transformations to critical token paths, increasing downstream task accuracy by 8–12% with only a 15% inference latency overhead. We detail the architectural innovations, training methodology, and benchmark results against comparable 1B–3B models.