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arturoornelasb/
triadic-gpt2-medium-v8

Name: triadic-gpt2-medium-v8
Availability: InStock

ontological-primitives

phase-transition

emergent-duality

representation-learning

openai-community/gpt2-medium

finetune

apache-2.0

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Triadic GPT-2 Medium (V8)

A 72-bit triadic projection head trained on top of frozen GPT-2 Medium (345M params).

Paper DOI

Repo DOI

What is this?

This model adds a triadic head to GPT-2 Medium that projects language model embeddings into 72-bit binary codes representing ontological primitives from the Emergent Duality framework.

Each bit corresponds to one of 72 semantic primitives organized in 6 algebraic layers (Boolean, Fuzzy, Ordinal, Modal, Trivalent, Probabilistic). The head learns to map continuous embeddings to discrete binary codes that preserve ontological structure.

Files

File	Description
`best.pt`	Final trained weights (best checkpoint at 250K steps)
`step_30000.pt`	Base checkpoint before phase transition (for X-ray reproducibility)
`run_config.json`	Full training configuration
`gold_extended_v7.json`	Gold standard: 72 primitives + extended concept targets

X-ray Reproducibility

step_30000.pt is the base checkpoint used for the telescopic X-ray analysis that revealed the cascade phase transition. To reproduce:

Resume training from step_30000.pt with save-every-5-steps
Run reptimeline on the resulting checkpoints
Observe the cascade activation at step ~31K (65/72 bits change in waves over ~145 steps)

See train_v8_xray.bat for exact commands.

Training

Base model: GPT-2 Medium (345M params, frozen)
Head: Deep triadic head (561K trainable params)
Activation: IFSQ (Integer Finite Scalar Quantization)
Dataset: WikiText-103
Steps: 250,000
Bit accuracy: 85.3% (test)
Subsumption: 95.7% (test)
Dead bits: 25/72
GPU: NVIDIA RTX 4060 Ti 16 GB

Key findings

Phase transition (cascade) at step ~31K with wave propagation over ~145 steps
65/72 bits change in waves of 1-7 bits; 7 scaffold bits remain stable throughout
Wave-FEP fit: R²=0.943, overdamped regime (d/w=1.34), dAIC=+92.2 (wave preferred over exponential)
Cross-architecture cascade pattern confirmed with v9 (GPT-Neo 125M)

Usage

python

import torch
from huggingface_hub import hf_hub_download

# Download checkpoint
ckpt_path = hf_hub_download("arturoornelasb/triadic-gpt2-medium-v8", "best.pt")
checkpoint = torch.load(ckpt_path, map_location="cpu")

# Or use with triadic_extractor (pip install reptimeline)
from triadic_extractor import TriadicExtractor
extractor = TriadicExtractor(n_bits=72)
snap = extractor.extract(ckpt_path, concepts, device='cuda')

Citation

bibtex

@misc{ornelas2026triadic,
  author       = {Ornelas Brand, J. Arturo},
  title        = {Triadic Duality: Ontological Primitives as Emergent Algebraic Structure in Neural Representations},
  year         = {2026},
  doi          = {10.5281/zenodo.19375167},
  publisher    = {Zenodo},
}

arturoornelasb/triadic-gpt2-medium-v8