HLM3 — Language Model
HLM3 is Qriton's language model built on polynomial Hopfield layers. Unlike transformer-based LLMs, HLM3 stores knowledge as discrete attractor basins that can be surgically edited.
Status
The validated public language path is HLM3-Mix K=16 with causal token mixing, FFN blocks, and weight tying. Larger checkpoints are validated through partner-specific runs before they are published as headline results.
What Makes It Different
HLM3 stores knowledge as discrete attractor basins — not distributed weights like transformers. This means individual behaviors can be edited without retraining the entire model.
Current Results
| Architecture | Dataset | Result | Status |
|---|---|---|---|
| HLM3-Mix K=16 | WikiText-103, 35M | val PPL 10.66 | validated |
| HLM3-Mix K=4 | WikiText-103, 35M | val PPL 11.44 | validated |
| HLM3 Base + FFN | WikiText-103, 35.9M | val PPL 48.3 | baseline |
The implementation uses a polynomial Hopfield core with a causal mixer over token positions. Reproduction material and checkpoint packages are shared through Early Access or commercial engagements.
Energy Language Integration
HLM3 supports all 36 Energy Language operations, including the full concept pipeline (capture, inject, blend, transplant), causal discovery (scan, intervene, counterfactual), and safety guards.
Custom Training
For organizations needing HLM3 trained on proprietary data at scale, we offer commercial training support and pilot project engagements. Get in touch.