Summary of Tokenformer: Rethinking Transformer Scaling with Tokenized Model Parameters, by Haiyang Wang et al.
TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters
by Haiyang Wang, Yue Fan, Muhammad Ferjad Naeem, Yongqin Xian, Jan Eric Lenssen, Liwei Wang, Federico Tombari, Bernt Schiele
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, the authors tackle a significant problem in transformer-based foundation models: their inability to scale efficiently. Currently, as transformers grow in size, retraining from scratch is necessary when architectural modifications are introduced. To overcome this limitation, the authors propose TokenFormer, an innovative architecture that leverages attention mechanisms for both token interactions and model parameter interactions. By treating model parameters as tokens, they replace traditional linear projections with a token-parameter attention layer. This allows for progressive and efficient scaling without requiring retraining from scratch. The paper demonstrates TokenFormer’s effectiveness by scaling the model from 124M to 1.4B parameters, achieving comparable performance to transformers trained from scratch while reducing training costs significantly. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TokenFormer is a new architecture that helps transformer-based foundation models scale efficiently. Right now, when we want to make these models bigger and better, we have to start over from scratch. But with TokenFormer, we can just add more pieces without having to do everything all over again. This makes it much faster and cheaper to train our models. |
Keywords
» Artificial intelligence » Attention » Token » Transformer
