7 papers
EntmaxKV: Support-Aware Decoding for Entmax Attention
Gonçalo Duarte, Miguel Couceiro, Marcos V. Treviso
Long-context decoding is increasingly limited by KV-cache memory traffic since each generated token attends over a cache whose size grows linearly with context length. Existing spa…
DashAttention: Differentiable and Adaptive Sparse Hierarchical Attention
Yuxiang Huang, Nuno M. T. Gonçalves, Federico Alvetreti +5
Current hierarchical attention methods, such as NSA and InfLLMv2, select the top-k relevant key-value (KV) blocks based on coarse attention scores and subsequently apply fine-grain…
AdaSplash-2: Faster Differentiable Sparse Attention
Nuno Gonçalves, Hugo Pitorro, Vlad Niculae +4
Sparse attention has been proposed as a way to alleviate the quadratic cost of transformers, a central bottleneck in long-context training. A promising line of work is -entmax…
AMALIA Technical Report: A Fully Open Source Large Language Model for European Portuguese
Afonso SimplÃcio, Gonçalo Vinagre, Miguel Moura Ramos +19
Despite rapid progress in open large language models (LLMs), European Portuguese (pt-PT) remains underrepresented in both training data and native evaluation, with machine-translat…
Long-Context Generalization with Sparse Attention
Pavlo Vasylenko, Hugo Pitorro, André F. T. Martins +1
Transformer-based architectures traditionally employ softmax to compute attention weights, which produces dense distributions over all tokens in a sequence. While effective in many…
AdaSplash: Adaptive Sparse Flash Attention
Nuno Gonçalves, Marcos Treviso, André F. T. Martins
The computational cost of softmax-based attention in transformers limits their applicability to long-context tasks. Adaptive sparsity, of which -entmax attention is an example,…