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Smol AI News explores hybrid SSM/Transformer models over pure architectures

A recent analysis suggests that hybrid architectures combining State Space Models (SSMs) with Transformers may outperform models that exclusively use one architecture. This approach aims to leverage the strengths of both, potentially leading to more efficient and capable AI systems. The findings indicate a promising direction for future model development, moving beyond the limitations of purely Transformer-based or SSM-based designs. AI

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COVERAGE [1]

  1. Smol AINews TIER_1 ·

    Hybrid SSM/Transformers > Pure SSMs/Pure Transformers

    **NVIDIA**'s Bryan Catanzaro highlights a new paper on **Mamba models**, showing that mixing Mamba and Transformer blocks outperforms either alone, with optimal attention below **20%**. **Mixture-of-Agents (MoA)** architecture improves LLM generation quality, scoring **65.1% on A…