At AtomixLabs, our research often focuses on the physical constraints of neural architectures. With AtomixS2-5M-v1.0, we wanted to explore the absolute floor of language acquisition: What happens when you restrict a model to just under 6 million parameters? At this extreme scale, a model does not have the capacity to memorize the internet, store vast encyclopedias of trivia, or comprehend deep physical-world mechanics. Instead, it becomes a pure engine of syntax and structure. This model is our attempt to build a highly active, fluent, and structurally sound micro-model that runs easily on almost any hardware—from older consumer GPUs to standard laptop processors. It is an exploration of parameter density and careful data curation over sheer scale. AtomixS2-5M is a standard decoder-only transformer, built with a very tight structural configuration: Parameter Count: 5.98 Million Layers: 4 Attention Heads: 8 Context Window: 512 Tokens When working with a parameter budget this constrained, traditional methods of tokenization become a major liability. Standard vocabularies often leak capacity by assigning precious embedding parameters to unused symbols, broken formatting…
Модальности:
Генерация текста
Задача: Генерация текста
Автор: AtomixLabs
Теги: llama, small, tiny, AtomixLabs, slm, SLM, small language model, language model
Лайков: 3 | Загрузок: 183
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
