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English(EN) I trained my own 150M non-Transformer language model from scratch on 300M tokens — WarpState

开发者从头开始训练了1.5亿参数非Transformer语言模型

一位独立开发者创建了一种名为WarpState的新语言模型架构,拥有1.5013亿个参数,并在约3亿个英文token上进行了训练。该模型通过在128个token的块内使用局部分块注意力(local tiled attention)和双快/慢张量内存系统来保留长序列信息,从而偏离了标准的Transformer架构。开发者在笔记本电脑GPU上训练了这个实验性模型,突显了其高效计算的潜力。 AI

影响 这项研究探索了Transformer的替代架构,可能带来更高效、更专业的语言模型。

排序理由 该条目描述了一种新颖的语言模型架构及其训练,属于研究范畴。[lever_c_demoted from research: ic=1 ai=1.0]

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开发者从头开始训练了1.5亿参数非Transformer语言模型

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该条目描述了一种新颖的语言模型架构及其训练,属于研究范畴。[lever_c_demoted from research: ic=1 ai=1.0]
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  1. r/OpenAI TIER_2 English(EN) · /u/zemondza ·

    我从头开始用3亿个token训练了自己的1.5亿参数非Transformer语言模型 — WarpState

    <table> <tr><td> <a href="https://www.reddit.com/r/OpenAI/comments/1w13r3j/i_trained_my_own_150m_nontransformer_language/"> <img alt="I trained my own 150M non-Transformer language model from scratch on 300M tokens — WarpState" src="https://preview.redd.it/3rmrnnuqq6mh1.png?width…