[NeurIPS 2021 Spotlight] & [IJCV 2024] SOFT: Softmax-free Transformer with Linear Complexity
-
Updated
Mar 16, 2024 - Python
#
linear-complexity
Here are 5 public repositories matching this topic...
PyTorch implementation of Mamba: Selective State Space Models for efficient linear-time sequence modeling. An alternative to Transformers with O(N) complexity.
deep-learning transformers pytorch ssm mamba state-space-models sequence-modeling linear-complexity selective-scan
-
Updated
Jul 15, 2026 - Python
FluidLM is a Transformer-free language model replacing O(N²) self-attention with reaction-diffusion PDEs - achieving O(N) scaling, adaptive computation, and no KV-cache.
machine-learning natural-language-processing deep-learning language-model fluid-mechanics continuous-time linear-complexity efficient-nlp non-transformer pde-dynamics
-
Updated
Aug 26, 2026 - Python
GQLSA: Grouped-Query Latent Sparse Attention — A hardware-native attention mechanism combining latent compression, grouped-query sharing, and block-sparse attention for linear O(T) complexity.
machine-learning deep-learning language-modeling pytorch artificial-intelligence transformer attention-mechanism research-paper efficient-transformers linear-complexity sparse-attention llm grouped-query-attention latent-compression hardware-native
-
Updated
Sep 8, 2026 - Python
PyWave
-
Updated
Dec 25, 2025 - Python
Add this topic to your repo
To associate your repository with the linear-complexity topic, visit your repo's landing page and select "manage topics."