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Awesome GitHub RepositoriesSparse Attention Modules
Optimized components that replace dense attention mechanisms with sparse alternatives to reduce computational overhead.
Distinguishing note: Specifically targets the replacement of dense layers with sparse variants, distinct from general sparse matrix operations.
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- deepspeedai/DeepSpeed
deepspeedai/DeepSpeed
41,638View on GitHubDeepSpeed is a high-performance library designed to scale deep learning model training and inference across massive clusters of GPUs and compute nodes. It provides a comprehensive suite of tools for distributed training, enabling the execution of models that exceed the memory capacity of single devices through advanced parameter partitioning, pipeline-based model parallelism, and memory-efficient state offloading. The framework distinguishes itself through specialized communication-efficient optimizers and hardware-aware acceleration techniques. By utilizing gradient compression, quantization
The framework reduces computational overhead in pre-trained models by replacing dense self-attention layers with optimized sparse attention modules.
Pythonbillion-parameterscompressiondata-parallelism
