DuoNeural/CrystalCall-Lorentz-ADS-7L
text-generation model by DuoNeural
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From the publisher
Model card & documentation
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DuoNeural Research Lab Aura✨, Jesse Caldwell, Archon, & Kestrel Website: duoneural.com • HuggingFace: huggingface.co/DuoNeural • Codebase: github.com/DuoNeural/Lattice-CDM [](https://opensource.org/licenses/Apache-2.0) [](#the-full-7-layer-inter-layer-cascade) [](#training-telemetry-and-validation) [](#lorentz-anti-de-sitter-geometry) CrystalCall-Lorentz-ADS-7L is DuoNeural's flagship 7-layer non-Euclidean Crystallographic Dynamics Model (CDM) (originating from DuoNeural's foundational Competitive Docking Memory framework, Zenodo doi:10.5281/zenodo.21158430), implementing the Lorentz Hyperboloid geometry with an Anti-de Sitter (AdS) Curvature Warp. Trained across 100,000 steps on a dedicated 32GB memory-expanded NVIDIA GeForce RTX 4080 Super cloud compute node (node p19970), CrystalCall-Lorentz-ADS-7L achieved: During step 100,000 validation telemetry, a complete 7-layer simultaneous Inter-Layer Cascade (ILC) was captured: CrystalCall-Lorentz-ADS-7L uses the vocabulary of Salesforce/xLAM-1b-fc-r (32,256 tokens). Parameter breakdown: CrystalCall-Lorentz-ADS-7L routes tokens on the upper sheet of a two-sheet hyperboloid…
Read the full model card ↗ · Preview checked 2026-10-30T08:08:26.311Z
Inside the original model card — Document outline
- CrystalCall-Lorentz-ADS-7L
- Executive Summary & Breakthrough Highlights
- The Full 7-Layer Inter-Layer Cascade
- Parameter Census & Glass-Box Transparency
- Lorentz Anti-de Sitter Geometry
- Training Telemetry and Dataset Grounding
- Native GGUF Inference & Numerical Parity (Kestrel Audit)
- CPU Numerical Parity Screen (Teacher-Forced Probes on Kilonova)
- Runtime Implementation Disclosures
- Citation & Lab Attribution
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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/CrystalCall-Lorentz-ADS-7L is a text-generation model repository published by DuoNeural on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- text-generation
- Library
- transformers
- Recent downloads (30 days)
- 0
- Cumulative likes
- 0
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 266,559,128
- Architecture
- CDMMoELM
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2026-10-30T06:34:57.000Z
- Last modified
- 2026-10-30T06:44:01.000Z
Compatibility and lineage
Language coverage was not reported.
Reported base models: Salesforce/xLAM-1b-fc-r
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 2026-10-30T06:49:24.903Z. Daily imports are snapshots, not real-time monitoring.
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