-
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 605 -
BitNet: Scaling 1-bit Transformers for Large Language Models
Paper • 2310.11453 • Published • 96 -
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
Paper • 2404.02258 • Published • 104 -
TransformerFAM: Feedback attention is working memory
Paper • 2404.09173 • Published • 43
Collections
Discover the best community collections!
Collections including paper arxiv:2406.07522
-
StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization
Paper • 2311.14495 • Published • 1 -
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
Paper • 2401.09417 • Published • 59 -
SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation
Paper • 2401.13560 • Published • 1 -
Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces
Paper • 2402.00789 • Published • 2
-
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
Paper • 2401.09417 • Published • 59 -
VMamba: Visual State Space Model
Paper • 2401.10166 • Published • 38 -
SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation
Paper • 2401.13560 • Published • 1 -
Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces
Paper • 2402.00789 • Published • 2
-
Trellis Networks for Sequence Modeling
Paper • 1810.06682 • Published • 1 -
Pruning Very Deep Neural Network Channels for Efficient Inference
Paper • 2211.08339 • Published • 1 -
LAPP: Layer Adaptive Progressive Pruning for Compressing CNNs from Scratch
Paper • 2309.14157 • Published • 1 -
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Paper • 2312.00752 • Published • 138
-
Scaling MLPs: A Tale of Inductive Bias
Paper • 2306.13575 • Published • 14 -
Trap of Feature Diversity in the Learning of MLPs
Paper • 2112.00980 • Published • 1 -
Understanding the Spectral Bias of Coordinate Based MLPs Via Training Dynamics
Paper • 2301.05816 • Published • 1 -
RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality?
Paper • 2108.04384 • Published • 1
-
Efficient Memory Management for Large Language Model Serving with PagedAttention
Paper • 2309.06180 • Published • 25 -
LM-Infinite: Simple On-the-Fly Length Generalization for Large Language Models
Paper • 2308.16137 • Published • 39 -
Scaling Transformer to 1M tokens and beyond with RMT
Paper • 2304.11062 • Published • 2 -
DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models
Paper • 2309.14509 • Published • 17
-
Instruction Pre-Training: Language Models are Supervised Multitask Learners
Paper • 2406.14491 • Published • 87 -
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Paper • 2405.21060 • Published • 64 -
Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models
Paper • 2405.20541 • Published • 22 -
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
Paper • 2406.01574 • Published • 44
-
mDPO: Conditional Preference Optimization for Multimodal Large Language Models
Paper • 2406.11839 • Published • 37 -
Pandora: Towards General World Model with Natural Language Actions and Video States
Paper • 2406.09455 • Published • 15 -
WPO: Enhancing RLHF with Weighted Preference Optimization
Paper • 2406.11827 • Published • 14 -
In-Context Editing: Learning Knowledge from Self-Induced Distributions
Paper • 2406.11194 • Published • 15