This is the Linux app named Sparse Attention whose latest release can be downloaded as sparse_attentionsourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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SCREENSHOTS:
Sparse Attention
DESCRIPTION:
Sparse Attention is OpenAI’s code release for the Sparse Transformer model, introduced in the paper Generating Long Sequences with Sparse Transformers. It explores how modifying the self-attention mechanism with sparse patterns can reduce the quadratic scaling of standard transformers, making it possible to model much longer sequences efficiently. The repository provides implementations of sparse attention layers, training code, and evaluation scripts for benchmark datasets. It highlights both fixed and learnable sparsity patterns that trade off computational cost and model expressiveness. By enabling tractable training on longer contexts, the project opened the door to applications in large-scale text and image generation. Though archived, it remains a key reference for efficient transformer research, influencing many later architectures that aim to extend sequence length while reducing compute.
Features
- Reference implementation of sparse transformer attention
- Efficient handling of long sequences by reducing quadratic cost
- Support for fixed and learnable sparse patterns
- Training and evaluation pipelines for benchmarks
- Example configs for reproducing paper experiments
- Foundation for later efficient transformer research
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/sparse-attention.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.