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AIQuiet 4d · day 4

MiMo-V2.6 tops open-weight LLM benchmarks with simple architecture

Sebastian Raschka highlights the model's ranking using basic Grouped Query Attention and Sliding Window mechanisms.

newest coverage 3d agosummary written 3d ago2 pieces455 likes & upvotes27 comments X

What to know

  • MiMo-V2.6 ranks first in open-weight language model benchmarks by weighted average, according to Raschka.
  • The model achieves top performance using straightforward techniques: Grouped Query Attention with minimal 128-token Sliding Window Attention.
  • The announcement suggests architectural simplicity need not sacrifice benchmark performance in open-source LLMs.

Sebastian Raschka Machine learning researcher

MiMo-V2.6 tops open-weight LLM benchmarks with simple architecture
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How it unfolded 1 development · click the chart to see its coverage posts

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Sep 22Sep 23Sep 24yesterdaynow · 8:29 AM ET
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    “MiMo-V2.6 is "simply" the best (for now). Despite its simple architecture design it's currently No.1 in the open-weight benchmarks (weighted average).”
    — Sebastian Raschka

    • MiMo-V2.6 is "simply" the best (for now). Despite its simple architecture design it's currently No.1 in the open-weight benchmarks (weighted average). With "simple," I mean a classic Grouped Query Attention (GQA) with Sliding Window Attention (SWA) at a tiny 128-token window

      @rasbtX3d ago455▲view on X ↗
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