3-Adic Hyperbolic VAE โ€” Canonical Checkpoint Archive

Archives the training-run checkpoints that CLAUDE.md in the source repo (https://github.com/gesttaltt/3-adic-ml) cites as evidence for its documented results (V15โ€“V23). These previously existed only on one local disk with no backup โ€” .gitignore in the source repo has referenced a checkpoint_hub download tool since early on, but that tool was never actually built. This repo is that archive, added retroactively.

Each folder has checkpoint.pt (best_Q.pt from the run), config.yaml (exact training config), and results.json where the run produced one.

Folder What it is Cited in CLAUDE.md as
v15.0_long_term_stability/ 2000-epoch long-horizon stability run V15.0 completed
v16.0_human_fine_tuning/ Fine-tuned on human TP53 codon windows V16.0 (Human TP53) completed
v17.0_rosetta_manifold/ Grand-master run: synthetic + human TP53 + bioactive peptides, single manifold Phase 17 "Rosetta Manifold"
v17.1_rosetta_manifold_resume/ Resume/continuation of the above Phase 17 resume
v21.0_clean_run/ Best checkpoint epoch 230/1000. ARI(prefix-3, v=0)=1.000, Spearman hierarchy=0.8335, Q=1.9755 "V21.0 Training Results"
v23.0_algebraic/ Best checkpoint epoch 210/1000. Adds algebraic structure signal (4-bit signature, Lagrangian dual ascent). Q=1.9698, hierarchy Spearman=0.8335 "V23.0 Training Results"

Note: v17.0_rosetta_manifold has no results.json (wasn't saved at the time of that run) -- config + checkpoint only.

Loading

import torch
ckpt = torch.load("v21.0_clean_run/checkpoint.pt", weights_only=True)

See the source repo's src/models/vae.py (TernaryVAEV6Controllable) for the model class, and the corresponding config.yaml in each folder for the exact architecture/training hyperparameters each checkpoint was produced with.

Related

For the external validation follow-up (does this architecture generalize to real, non-synthetic biological data?), see geestaltt/3-adic-vae-cytochrome-c -- a separate, negative result on real cytochrome c phylogeny.

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