Distilling Kimi Delta Attention into AFM-4.5B (and the Tool We Used to Do It)

Moonshot AI has introduced Kimi Delta Attention (KDA) as an extension of Gated DeltaNet, offering promising results particularly in local and global attention hybrid arrangements. The author experiments with converting the AFM-4.5B-Base model into a hybrid KDA and full-attention transformer using knowledge distillation, inspired by the RADLADS paper. The process involves creating a student model, modifying the attention architecture, and using a streamlined distillation pipeline to achieve efficient alignment with the teacher model. The experiments highlight the advantages of KDA in certain contexts, particularly long sequence lengths, while also noting performance variations based on attention configurations. The introduction of the open-source DistillKit facilitates these experiments, enabling both online and offline distillation with various loss functions and model configurations, demonstrating the potential for faster and more efficient model training and inference.

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