DuoNeural/Sub-Lattice-v3-Poincare-18M

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Sub-Lattice-v3-18M-Poincare-Hybrid is an ultra-compact, 3-layer, 18.23M-parameter (5.52M non-embedding backbone) hybrid Causal-Attention + Hyperbolic Poincaré Crystallographic Mixture-of-Experts (CDMMoELM v3) language model distilled from HuggingFaceTB/SmolLM2-360M-Instruct (361.8M parameters — 19.8× total compression / 65.6× vs. backbone) on a 50/50 hybrid stream of FineWeb-Edu and SmolTalk (apigen-80k, everyday-conversations, smol-constraints). Building upon our v1 DuoNeural/Sub-Lattice-17M-Poincare-Instruct (dff = 768, fixed curvature c = 1.4427, SmolTalk-only), Sub-Lattice v3 upgrades the 3-layer dmodel = 256 student with: Despite dedicating half of its training tokens to open-domain FineWeb-Edu prose, Sub-Lattice v3 (5.52M backbone) not only crushes its 361.8M Euclidean teacher on held-out apigen-80k JSON function calling (80.0% vs. 26.7% valid JSON), but surpasses v1 Sub-Lattice on exact tool-name accuracy (56.7% vs. 40.0%, 3.4× higher than the 361.8M teacher's 16.7%) and achieves a better Teacher-Scored Chat Generation Perplexity (1.41) than the Teacher's own greedy generations (1.56)! In the 3-layer Sub-Lattice v3 architecture, inter-layer Crystal Telepathy (‖Wtele‖F = 30.63 on L01…

DuoNeural/Sub-Lattice-v3-Poincare-18M on Hugging Face A short preview, not the full document.

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  1. 💎 DuoNeural/Sub-Lattice-v3-18M-Poincare-Hybrid
  2. 📊 Comprehensive Benchmark Suite (evallatticev3suite.py)
  3. 🔬 Key Scientific Discoveries in Sub-Lattice v3
  4. 1. The Same-Slot Synchronization Law (Discovery #4 — Slot 4 Master Coordinator)
  5. 2. Why Sub-Lattice v3 (5.52M Backbone) Beats Sub-Lattice v1 on Exact Tool Name Accuracy (56.7% vs. 4
  6. 💻 PyTorch Reference Inference (CDMMoELM v3)
  7. ⚡ Native llama.cpp GGUF Quantizations & CPU/CUDA Benchmarks (Experimental Research Artifacts)
  8. 1. Included Tokenizer-Equipped GGUF Artifacts & Fidelity Screen (Sub-Lattice-v3-18M)
  9. 2. Matched CPU (Core i7-6700HQ) vs. CUDA (GTX 1070 8 GiB) Throughput
  10. 3. Building & Running Patched llama.cpp (llama-cdm.patch)
  11. 📚 DuoNeural Research Canon & Citation

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Model overview

DuoNeural/Sub-Lattice-v3-Poincare-18M is a task-unspecified model repository published by DuoNeural on Hugging Face. The source reports the pytorch library.

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Model facts

Task
Not reported
Library
pytorch
Recent downloads (30 days)
0
Cumulative likes
0
Hugging Face trending score
0
Reported safetensors parameters
30,422,264
Architecture
Not reported
License
apache-2.0
Access
Not gated by Hugging Face
Created
2026-10-24T06:29:37.000Z
Last modified
2026-10-24T06:29:41.000Z

Compatibility and lineage

Reported languages: en

Reported base models: HuggingFaceTB/SmolLM2-360M-Instruct

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