Tokenformer: The Next Generation of Transformers?
In this video we dive into Tokenformer, a novel AI model architecture which suggests a fascinating change to the Transformer architecture. Tokenformer was introduced in a recent AI research paper, titled: "Tokenformer: Rethinking Transformer Scaling with Tokenized Model Parameters", showing a remarkable potential for saving training costs. When increasing models size, Transformer models need to be trained from scratch, but Tokenformer models are built to grow incrementally and thus saving cost of training the entire model from scratch.
First we conduct a high-level comparison between the standard Transformer architecture, to Tokenformer. Tokenformer novel idea is to use the attention mechanism, not only for token to token interactions, but also for token to parameter interactions, instead of linear projection. We explain in more detail how it is built in the video.
Paper - https://arxiv.org/abs/2410.23168
Code - https://github.com/Haiyang-W/TokenFormer
Blog - https://aipapersacademy.com/tokenformer/
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Chapters:
0:00 Introduction
1:05 Transformer vs Tokenformer
3:24 Tokenformer Architecture
5:38 Results