Ternary Mamba-2 1.3B models reach 48.1% zero-shot accuracy via QAT from pretrained checkpoints in 102M tokens, close to Bi-Mamba, with 3.61x compression.
Spike-temporal latent representation for energy-efficient event-to-video reconstruction
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
MAR integrates SSMs and sparsification with new ATMN neurons and SBDS distillation to produce efficient LLMs that match dense-model performance at substantially lower inference energy.
SpikingMamba distills Mamba into an SNN LLM achieving 4.76x energy savings with a 4.78% zero-shot accuracy gap that narrows to 2.23% after RL.
citing papers explorer
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Ternary Mamba: Grouped Quantization-Aware Training of W1.58A16 State Space Models
Ternary Mamba-2 1.3B models reach 48.1% zero-shot accuracy via QAT from pretrained checkpoints in 102M tokens, close to Bi-Mamba, with 3.61x compression.
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MAR: Efficient Large Language Models via Module-aware Architecture Refinement
MAR integrates SSMs and sparsification with new ATMN neurons and SBDS distillation to produce efficient LLMs that match dense-model performance at substantially lower inference energy.
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SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba
SpikingMamba distills Mamba into an SNN LLM achieving 4.76x energy savings with a 4.78% zero-shot accuracy gap that narrows to 2.23% after RL.