State-Space Models / Mamba

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  "llmSummary": "Comprehensive analysis of state-space models (SSMs) like Mamba as transformer alternatives, documenting that Mamba-3B matches Transformer-6B perplexity with 5x throughput but lags on in-context learning (MMLU: 46.3% vs 51.2% at 8B scale). Hybrid architectures combining 43% SSM + 7% attention outperform pure transformers (+1.3 points) while maintaining efficiency gains, with estimated 45% probability of hybrids becoming dominant vs 35% for pure transformers.",
  "description": "Analysis of Mamba and other state-space model architectures as alternatives to transformers. SSMs achieve 5x higher inference throughput with linear O(n) complexity versus quadratic O(n^2) attention. Mamba-3B matches Transformer-6B perplexity while Jamba 1.5 outperforms Llama-3.1-70B on Arena Hard. However, pure SSMs lag on in-context learning tasks, making hybrids increasingly dominant.",
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