Papers with Mamba-Shedder
Mamba-Shedder: Post-Transformer Compression for Efficient Selective Structured State Space Models (2025.naacl-long)
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| Challenge: | Large pre-trained models have achieved outstanding results in sequence modeling . alternative architectures, such as Selective Structured State Space Models (SSMs), have been proposed to address these inefficiencies. |
| Approach: | They propose to reduce the size and computational overhead of large pre-trained models by removing selected components at different granularities. |
| Outcome: | The proposed models achieve a speedup of up to 1.4x during inference while maintaining accuracy. |