HLM Models
Qriton builds Hopfield-layer models for applications that need editable behavior, validation traces, and deployment across cloud, workstation, and edge hardware. The public model suite is organized by deployment tier, while implementation details, partner datasets, and reproduction packages are shared through Early Access or commercial engagements.
The model suite at a glance
Capability and model size grow toward the apex. Deployment density and cost-effectiveness grow toward the base. A typical deployment combines tiers: small devices monitor local signals, edge gateways aggregate events, and larger models handle the cases that require deeper analysis.
Platform architecture
The diagram is intentionally product-level: it shows how data sources, modality-shaped frontends, Hopfield layers, audit signals, and enterprise outputs fit together without exposing partner-specific implementation details.
The four tiers
Tier 1 - Reasoning and Language
Cloud and GPU-hosted HLM3 models for research analysis, regulated decision-support pipelines, complex generation, and language-heavy workflows.
Tier 2 - Specialized Perception
Workstation or single-GPU models for image, spatial, and audio workflows. These models keep the Hopfield core but use frontends and mixers that match the data geometry.
Tier 3 - Edge Inference
Gateway and MCU-class models for sensor fusion, anomaly detection, human activity recognition, and low-power local classification. The emphasis is local decisioning plus audit-friendly deployment.
Tier 4 - Ultra-Edge
KB-scale classifiers for very small devices and constrained sensing applications. These are intended for narrow, retrained tasks where footprint and power budget matter more than model breadth.
Model family table
| Tier | Model | Domain | Public status |
|---|---|---|---|
| 1 | HLM3 | Language and reasoning | K=16 path validated; val PPL 10.66 |
| 2 | HLM-Vision | 2D image classification | Demo-ready; CIFAR-10 83.91% |
| 2 | HLM-Spatial | 3D perception | Demo-ready checkpoints |
| 2 | HLM-Audio | Speech and audio | TTS demo-ready; val_loss 0.3845 |
| 3 | HLM-Micro | Edge sensor fusion | HAR v1 93.52%; anomaly v1 99.00% synthetic |
| 4 | HLM-Nano | Ultra-edge classification | KB-scale Early Access |
Available checkpoints
Public checkpoint availability is summarized in the Model Zoo. Current validation numbers are listed on the Validation Summary. The Overview page deliberately avoids raw recipes, unpublished roadmaps, and partner-specific manifests.
Why the same family across tiers?
The shared part is the Hopfield energy-landscape core. The frontend is allowed to change with the data shape: language, images, audio, spatial data, and edge sensors should not be forced through the same raw representation.
This gives the platform a consistent operating surface without pretending that all modalities have identical geometry:
- Shared validation and audit concepts across model families
- Modality-specific frontends where the data demands them
- A single commercial integration path for cloud, edge, and ultra-edge pilots
How HLM differs from attention-first models
Attention-first systems are strong general-purpose predictors, but they do not natively expose a stable, editable energy landscape as the main operating surface. HLM is built around attractor dynamics: the same inference object that produces a prediction can also be inspected, constrained, and audited.
That is the public point to make. The lower-level training recipes, basin counts, and experimental variants stay out of the internet-facing overview.
Commercial and pilot projects
For organizations that need custom training, deployment support, or a pilot project in industrial, medical, edge sensing, or regulated-AI settings, contact Qriton directly.
Contact us to discuss requirements.