Implementation of the sparse attention pattern proposed by the Deepseek team in their "Native Sparse Attention" paper
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Updated
Aug 15, 2025 - Python
Implementation of the sparse attention pattern proposed by the Deepseek team in their "Native Sparse Attention" paper
[ICML2025] SpargeAttention: A training-free sparse attention that accelerates any model inference.
LongLive: Real-time Interactive Long Video Generation
Fast Multi-dimensional Sparse Attention
[NeurIPS 2025] Radial Attention: O(nlogn) Sparse Attention with Energy Decay for Long Video Generation
[ICML2025, NeurIPS2025 Spotlight] Sparse VideoGen 1 & 2: Accelerating Video Diffusion Transformers with Sparse Attention
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
[ICML 2025 Spotlight] ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference
Efficient triton implementation of Native Sparse Attention.
[CoLM'25] The official implementation of the paper <MoA: Mixture of Sparse Attention for Automatic Large Language Model Compression>
Code for paper: [ICLR2025 Oral] FlexPrefill: A Context-Aware Sparse Attention Mechanism for Efficient Long-Sequence Inference
SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse–Linear Attention
[Arxiv 2025] SparseD: Sparse Attention for Diffusion Language Models
[TIP-2025] Official Pytorch implementation of "Structural Similarity-Inspired Unfolding for Lightweight Image Super-Resolution"
Demo code for CVPR2023 paper "Sparsifiner: Learning Sparse Instance-Dependent Attention for Efficient Vision Transformers"
Dynamic Attention Mask (DAM) generate adaptive sparse attention masks per layer and head for Transformer models, enabling long-context inference with lower compute and memory overhead without fine-tuning.
The code implementation of paper "VORTA: Efficient Video Diffusion via Routing Sparse Attention"
Building Native Sparse Attention
Toy Hydra prototypes: SSM + sparse attention + MoE + memory; synthetic benchmarks. Paper: https://arxiv.org/abs/2508.15099
Classification binaire avec architecture Sparse Attention pour données tabulaires. Optimisation automatique des hyperparamètres via Optuna. Testé sur datasets de churn télécommunications et bancaire.
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