Optimizing Large Language Model Training Using FP4 Quantization Paper • 2501.17116 • Published Jan 28 • 37
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking Paper • 2501.04519 • Published Jan 8 • 275
Cautious Optimizers: Improving Training with One Line of Code Paper • 2411.16085 • Published Nov 25, 2024 • 21
nGPT: Normalized Transformer with Representation Learning on the Hypersphere Paper • 2410.01131 • Published Oct 1, 2024 • 10
Addition is All You Need for Energy-efficient Language Models Paper • 2410.00907 • Published Oct 1, 2024 • 150 • 17
Addition is All You Need for Energy-efficient Language Models Paper • 2410.00907 • Published Oct 1, 2024 • 150
Q-Sparse: All Large Language Models can be Fully Sparsely-Activated Paper • 2407.10969 • Published Jul 15, 2024 • 23
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark Paper • 2406.01574 • Published Jun 3, 2024 • 47
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality Paper • 2405.21060 • Published May 31, 2024 • 68
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality Paper • 2405.21060 • Published May 31, 2024 • 68 • 3
Kangaroo: Lossless Self-Speculative Decoding via Double Early Exiting Paper • 2404.18911 • Published Apr 29, 2024 • 31 • 2
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits Paper • 2402.17764 • Published Feb 27, 2024 • 613 • 142