AI/ML●Mid
Lila-E8 – 40M Parameter LLM with 0.37 Loss via E8 Lattice Attention
E8 lattice geometry replaces attention—clever math, but TinyStories 0.37 loss needs context.
Big BrainWizardry
bootstraptor
103mo ago
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3 starsC++
Jam Storyteller – Attention? Memory Is All You Need
by amthorn·Mar 1, 2026·2 points·1 comment
AI Analysis
●●●BangerWizardryBig BrainZero to One
Transformer without multiplications: 262K ops reduced to additions via algebraic mixing matrix.
Strengths
- •Genuine architectural novelty: fixed mixing matrix from binary theorem of algebraic constant eliminates all learned weights in attention stage—mathematically elegant, not an approximation
- •Runs 100M-parameter model on CPU in real time (98MB quantized), no GPU or PyTorch required—validates the constraint works
- •Clear validation: achieves comparable loss to standard transformer on same dataset and training regime
Weaknesses
- •Niche audience: researchers and efficiency hackers only; unclear if technique scales beyond 100M or generalizes to instruction tuning
- •No benchmarks against other CPU-efficient approaches (quantization, distillation, MobileNet-style compression)
Category
Target Audience
ML researchers, engineers interested in alternative architectures, efficiency-focused practitioners
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