Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
Paper
•
2401.02994
•
Published
•
47
new architecture
-
-
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 50 -
Repeat After Me: Transformers are Better than State Space Models at Copying
Paper • 2402.01032 • Published • 22 -
BlackMamba: Mixture of Experts for State-Space Models
Paper • 2402.01771 • Published • 23 -
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
Paper • 2402.04248 • Published • 30 -
KAN: Kolmogorov-Arnold Networks
Paper • 2404.19756 • Published • 108 -
Zamba: A Compact 7B SSM Hybrid Model
Paper • 2405.16712 • Published • 21 -
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Paper • 2405.21060 • Published • 63 -
Block Transformer: Global-to-Local Language Modeling for Fast Inference
Paper • 2406.02657 • Published • 36 -
Breaking the Attention Bottleneck
Paper • 2406.10906 • Published • 4 -
Learning to (Learn at Test Time): RNNs with Expressive Hidden States
Paper • 2407.04620 • Published • 27 -
Jamba-1.5: Hybrid Transformer-Mamba Models at Scale
Paper • 2408.12570 • Published • 29 -
A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond
Paper • 2410.02362 • Published • 16 -
Differential Transformer
Paper • 2410.05258 • Published • 165 -
GPT or BERT: why not both?
Paper • 2410.24159 • Published • 11