Revolutionizing AI Scaling: Token Forer Transformer Modification
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Today, we delve into the world of cutting-edge AI with Yannic Kilcher's review of "Token Forer: Rethinking Transformer scaling with tokenized model parameters." Developed by a powerhouse collaboration between MOX Plunk Institute for Informatics, Google, and Peking University, this paper introduces Token Forer, a bold modification of the Transformer architecture. By treating model parameters as tokens, this new approach promises a revolutionary level of flexibility in scaling, allowing for the addition of parameters to a trained model without the need to start from scratch. It's like adding nitrous to a finely-tuned engine without having to rebuild it from the ground up.
Traditionally, Transformers have been shackled by fixed parameters in linear projections, forcing a complete retraining of the model for any modifications. Token Forer changes the game by leveraging attention mechanisms not just for token interactions, but also for interactions between tokens and model parameters. This shift from linear interactions to attention mechanisms opens up a world of possibilities, enabling the seamless integration of additional parameters without the hassle of altering model dimensions. It's like upgrading your car's engine without having to change the entire chassis.
However, as Yannic Kilcher dives deeper into the paper, he uncovers some discrepancies that raise eyebrows. The claimed performance improvements of Token Forer compared to traditional Transformers come into question, especially when analyzing the training data sizes. The team's findings reveal that even at the smallest level, Token Forer seems to outperform traditional methods, sparking skepticism about the validity of the results. It's like claiming your souped-up sports car is faster than a Formula 1 car, but the numbers don't quite add up.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Watch TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters (Paper Explained) on Youtube
Viewer Reactions for TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters (Paper Explained)
The 757M model is worse but takes less than half the compute to train due to upscaling from smaller models.
The idea of swapping out tokenized parameters based on the input is interesting for creating a kind of memory.
The video was entertaining and structured like a stand-up joke.
The paper focuses on hours to train, not just the number of tokens.
The stability-plasticity dilemma in machine learning should be highlighted more in papers.
The comparison between training a large model from scratch and scaling from a small model over training epochs is requested.
Models tend to compensate in the long run regardless of how they are trained.
Exploring deeper recursion with little sub-transformers in transformers is suggested.
The "pattention" is equivalent to a MLP, surprising some ML researchers.
The QKV projection is generated using another attention mechanism, and there are thoughts on integrating curriculum learning.
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