LAWCAT: Efficient Distillation from Quadratic to Linear Attention with Convolution across Tokens for Long Context Modeling
PositiveArtificial Intelligence
The new LAWCAT model presents an innovative approach to improve long-context modeling by efficiently distilling quadratic attention into linear attention using convolution across tokens. This advancement addresses the computational challenges faced by traditional transformer architectures, making it a promising solution for latency-sensitive applications.
— Curated by the World Pulse Now AI Editorial System
