Uni-MoE-2.0-Omni: Scaling Language-Centric Omnimodal Large Model with Advanced MoE, Training and Data Paper • 2511.12609 • Published 22 days ago • 102
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models Paper • 2508.09834 • Published Aug 13 • 53
ConPET: Continual Parameter-Efficient Tuning for Large Language Models Paper • 2309.14763 • Published Sep 26, 2023 • 1
ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs Paper • 2402.03804 • Published Feb 6, 2024 • 4
ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models Paper • 2402.13516 • Published Feb 21, 2024 • 1
BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity Paper • 2507.08771 • Published Jul 11 • 9
BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity Paper • 2507.08771 • Published Jul 11 • 9
BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity Paper • 2507.08771 • Published Jul 11 • 9 • 1