SLaB compresses LLM weights via sparse-lowrank-binary decomposition guided by activation-aware scores, achieving up to 36% lower perplexity than prior methods at 50% compression on Llama models.
Winogrande: An adversarial winograd schema challenge at scale,
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Dynamic nested hierarchies let models self-adjust their multi-level optimization structures to support lifelong learning and adaptation to shifting data distributions.
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SLaB: Sparse-Lowrank-Binary Decomposition for Efficient Large Language Models
SLaB compresses LLM weights via sparse-lowrank-binary decomposition guided by activation-aware scores, achieving up to 36% lower perplexity than prior methods at 50% compression on Llama models.
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Dynamic Nested Hierarchies: Pioneering Self-Evolution in Machine Learning Architectures for Lifelong Intelligence
Dynamic nested hierarchies let models self-adjust their multi-level optimization structures to support lifelong learning and adaptation to shifting data distributions.