Differential Transformers present a novel attention mechanism that reduces noise by splitting Query-Key pairs and implementing a subtraction mechanism inspired by Active Noise Cancellation. This appro
ach achieves better performance than standard transformers, especially in low-bit quantization scenarios, while maintaining similar gradient flow characteristics.
Reasons to Read -- Learn:
how Differential Transformers achieve better performance with 4-bit quantization compared to regular Transformers with 6-bit quantization, offering practical insights into model efficiency
complete implementation of the Differential Transformer architecture, including detailed code examples and mathematical explanations of the attention mechanism
how Active Noise Cancellation principles from electrical engineering can be applied to improve attention mechanisms in transformer models
9 min readauthor: Shubh Mishra
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