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A New Approach to Attention — Differential Transformers | Paper Walkthrough and PyTorch Implementation
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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