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AI model explores quaternion math for attention transformer architecture

A user explored the possibility of using quaternion algebra for attention transformers, conversing with a local Gemma 4:26b model. The model suggested it might be feasible and offer benefits, but warned that the inherent trigonometric functions in quaternion multiplication would make training at scale extremely difficult. This exploration highlights creative approaches to transformer architecture design. AI

影响 Explores novel mathematical foundations for transformer architectures, potentially inspiring future research.

排序理由 User explores a novel mathematical approach for AI model architecture in a personal blog post.

在 Mastodon — fosstodon.org 阅读 →

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AI model explores quaternion math for attention transformer architecture

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  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    I had a thought, "Can I use unit quaternion multiplication and exponentiation to create a matrix algebra I could build an attention transformer with?". I don't

    I had a thought, "Can I use unit quaternion multiplication and exponentiation to create a matrix algebra I could build an attention transformer with?". I don't have any people around anymore to even pose such a thought experiment to, so I conversed with my local copy of Gemma4:26…