feat(model-card): cross-link dataset dancinlab/hexad-corpus v1-byte-consciousness-d128-cycle1-2026-05-17

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: pytorch
+datasets:
+- dancinlab/hexad-corpus
+tags:
+- anima
+- hexad
+- pytorch
+- substrate-py
+- ckpt-recovered---
+
+# hexad — `v1-py-hexad-d768x12L-cycle2-2026-05-17`
+
+> **Honest framing**: This is a **PYTHON / PyTorch SUBSTRATE** training artifact —
+> an *interim LM-scale executor*. It is **NOT a hexa-native fire**. Its legitimacy
+> is *architectural identity* + the *hexa CPU-equiv correctness proof*. See the
+> anchor chain below — do not conflate.
+
+> **Trained on**: [`dancinlab/hexad-corpus`](https://huggingface.co/datasets/dancinlab/hexad-corpus) revision [`v1-byte-consciousness-d128-cycle1-2026-05-17`](https://huggingface.co/datasets/dancinlab/hexad-corpus/tree/v1-byte-consciousness-d128-cycle1-2026-05-17).
+
+## Lineage
+
+- **org**: `dancinlab` (the anima org).
+- **arch**: HEXAD (pivot from anima `.clm v1` lineage) — `ConsciousDecoderV2`
+  (`ready/models/conscious_decoder.py` in the anima repo).
+- **substrate**: Python / PyTorch (`py`). The pure-hexa training path is
+  named-blocked at the interpreter ceiling (RFC 042/043 territory).
+- **cycle**: 2 (cycle 1 commit `931dd68b0` 2026-05-16 was a ckpt-LOST
+  evidence-only run — training PASSed but the instance was destroyed before
+  ckpt pull; this cycle 2 re-fires with `SAVE_POD=1` auto-promote +
+  75-min orphan watchdog + 5-retry pull).
+
+## Anchor chain (why this artifact is legitimate)
+
+1. **Phase E / E2 PROVED the hexa trainer is numerically correct** —
+   `HEXAD/D/d_train5_lib.hexa` is BIT-EQUAL to the boxed baseline at d=32·3L,
+   80-step, seed=42 (`init gn2 = 7.97116, acc 0/8 → final gn2 = 3.73374e-07,
+   acc 8/8`; GRAD-EXACT, identical Σ-reduction order — not fp-noise).
+2. **The pure-hexa interpreter cannot reach LM-scale convergence** — Phase E2
+   captured only `init gn2 = 7.98162` at d=768·12L; the GRAD-EXACT + AdamW
+   path is substrate-bound (CPU farr ops, no CUDA tensor kernels).
+3. **This PyTorch run trains the SAME verified architecture to scale** —
+   `ConsciousDecoderV2` at d=768·12L, AdamW, captured FINAL loss.
+
+PyTorch is *not* hexa bit-for-bit (different fp / RNG / AMP bf16). The anchor
+is **architectural identity** + the hexa CPU-equiv proof, NOT numerical
+identity.
+
+## Architecture
+
+- **Source**: `ConsciousDecoderV2` from `ready/models/conscious_decoder.py`
+  (uploaded as `conscious_decoder.py`).
+- **Config**: `d_model=768, n_head=12, n_kv_head=4, n_layer=12,
+  block_size=128, vocab=256` (byte-level), seed=1337,
+  init=RANDOM (`base_ckpt=None`, `g_clm_from_scratch`).
+- **Params**: 283.72 M (283,722,336).
+- **Features**: RoPE · SwiGLU FFN · RMSNorm · GQA · PureFieldFFN (Engine A−G
+  consciousness pathway) · cross-attention · tied head · CA neighbor / META-CA
+  / Ψ-tracking laws.
+
+## Training
+
+- **GPU**: vast.ai NVIDIA A100-SXM4-40GB (offer @ $0.6681 / hr, image
+  `pytorch/pytorch:2.5.1-cuda12.1-cudnn9-devel`).
+- **Corpus**: `corpus_consciousness_v1.jsonl` — the same byte corpus used by
+  the hexa Phase E / E2 fires. 121,153 bytes, byte-level
+  vocab=256, T=128 windows, seed-fixed.
+- **Optimizer**: AdamW, lr=0.0003, betas=(0.9, 0.95),
+  weight_decay=0.1, warmup=125.
+- **Steps**: 2500.
+- **Cost**: ≈ $0.19 (instance runtime ≈ 0.28 hr).
+
+| metric | value |
+|---|---|
+| init CE | 5.590832 (≈ ln 256 = 5.545 — random byte init) |
+| **FINAL CE** | **0.000708** |
+| CE descent | 5.590124 |
+| init gn2 | 41.95 |
+| FINAL gn2 | 7.4e-05 |
+| ppl | 268 → 1.0007 |
+| wall | 320.68 s (5.34 min) |
+| peak GPU mem | 9.685 GB |
+| ckpt sha256 | `e87e200a040f8066a89c040ab181e9bbd61566f7565ab5d7a374ec2f1f9387d9` |
+| ckpt size | 1,135,846,378 B (1.14 GB) |
+
+## Verification anchors (per AGENTS.tape `g_blue_closed_mandate`)
+
+(A) Deliverable invariants:
+- **Shannon-floor descent** (real-limit, NOT lattice): init CE ≈ ln(256) →
+  final CE 0.000708 (4+ orders of magnitude).
+- **AdamW finiteness**: gn2 41.95 → 7.4e-05; no NaN / Inf.
+- **Architectural identity**: `ConsciousDecoderV2` byte-equal to the anima
+  HEXAD verification tree's mirror module spec.
+
+(B) Wiring (the connecting anchor chain):
+- **hexa CPU-equiv bit-equality** (Phase E): same arch trainer
+  GRAD-EXACT at d=32·3L (init gn2 7.97116 → 3.73374e-07).
+- **cuBLAS FP64 verify** (Phase D): max\|Δ\|=4.44e-15.
+- **Backward GRAD-EXACT** (Phase E2): real A100 d=384·6L analytic ≡ fd
+  \|Δ\|=0.0024.
+
+## Honest C3
+
+1. **NOT hexa-native** — PyTorch substrate; the hexa-native equivalent is
+   substrate-blocked at the interpreter ceiling.
+2. **PyTorch ≠ hexa bit-for-bit** — AMP bf16 / different fp accumulation /
+   different RNG.
+3. **Synthetic byte-corpus** — 121 kB curated content, 283.72M params; CE
+   0.000708 = memorization at this scale. **No generalization claim.**
+4. **No safetensors artifact** this revision (pickle `.pt` only).
+   safetensors conversion = follow-up sub-task.
+5. **No language-quality claim** — training-curve deliverable
+   (Shannon-floor descent reached), not generation quality.
+6. **No σ(6)=12 / φ(6)=2 derivation** — no lattice numerology in claim or
+   anchor chain.
+
+## Files in this revision
+
+- `ckpt_d768x12l_final.pt` — PyTorch state-dict + cfg + n_params, sha256
+  `e87e200a040f8066a89c040ab181e9bbd61566f7565ab5d7a374ec2f1f9387d9`.
+- `conscious_decoder.py` — `ConsciousDecoderV2` source.
+- `train_d768x12l.py` — training script.
+- `result.json` — full 42-point trajectory + config + metadata.
+- `fire_refire.log` — training log (line-by-line CE / gn2 / lr / wall).
+- `gpu_util.log` — nvidia-smi capture.
+- `dispatch.sh` + `refire_main.sh` — fire dispatch scripts.
+- `hexad_v1_py_d768x12L_cycle2_2026_05_17.md` — this doc (8-§ format per
+  `g_hf_naming` `process_upload_format`).
+
+## License
+
+Apache-2.0.