DuoNeural/Sub-Lattice-v3-Poincare-18M
AI model by DuoNeural
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Model card & documentation
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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…
Read the full model card ↗ · Preview checked 2026-10-28T20:50:41.663Z
Inside the original model card — Document outline
- 💎 DuoNeural/Sub-Lattice-v3-18M-Poincare-Hybrid
- 📊 Comprehensive Benchmark Suite (evallatticev3suite.py)
- 🔬 Key Scientific Discoveries in Sub-Lattice v3
- 1. The Same-Slot Synchronization Law (Discovery #4 — Slot 4 Master Coordinator)
- 2. Why Sub-Lattice v3 (5.52M Backbone) Beats Sub-Lattice v1 on Exact Tool Name Accuracy (56.7% vs. 4
- 💻 PyTorch Reference Inference (CDMMoELM v3)
- ⚡ Native llama.cpp GGUF Quantizations & CPU/CUDA Benchmarks (Experimental Research Artifacts)
- 1. Included Tokenizer-Equipped GGUF Artifacts & Fidelity Screen (Sub-Lattice-v3-18M)
- 2. Matched CPU (Core i7-6700HQ) vs. CUDA (GTX 1070 8 GiB) Throughput
- 3. Building & Running Patched llama.cpp (llama-cdm.patch)
- 📚 DuoNeural Research Canon & Citation
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Documentation belongs to its respective authors. Reported project/model license: apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
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.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
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
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-10-24T06:46:27.670Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.