GHOST Prunes Mamba2 Hidden States Efficiently
β‘ 30-Second TL;DR
What Changed
Structured pruning via forward-pass controllability and observability metrics
Why It Matters
Researchers and Mamba2 users benefit from efficient state compression without retraining or backprop, enabling smaller, faster models. It matters for deploying large SSMs on edge devices with minimal accuracy loss. This could accelerate Mamba2 adoption in resource-limited settings and inspire pruning for other SSMs.
What To Do Next
Evaluate benchmark claims against your own use cases before adoption.
Key Points
- β’Structured pruning via forward-pass controllability and observability metrics
- β’Avoids backpropagation for pruning process
- β’50% state reduction with ~1 PPL rise on WikiText-2 for 130M-2.7B models
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