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pip install git+https://github.com/AbstractEyes/alephllm, package
geolip.alephllm). This repo holds what training produces, one prefix
per craft:<craft>/manifest.json what is trained, what is planned,
tokens run, phase statuses, ckpt index
<craft>/checkpoints/step_XXXXXXXX.safetensors bf16 weights
<craft>/checkpoints/fp8/step_XXXXXXXX.safetensors fp8-e4m3 shipping variant
(inference testing only — never train from these)
<craft>/resume/latest.pt full resume state: fp32 model, Muon+Adam
optimizer states, data-stream position, RNG
<craft>/runs/ TensorBoard event files (rendered in the
Training metrics tab)train/grad_norm, clip 1.0) first crossed the clip at step 17,600 —
inside the fineweb phase, on the RTX PRO 6000, while it still led — then
escalated stage by stage: medians 0.29 on fineweb → 6.5 (s0) → 2.4 → 12 → 13
→ 24.5 → 126 (s5; nine steps above 1,000, max 11,877) → 471 (s6; max 41,070)
→ 162 → 145 → 167 → 215 (anneal_mix), clipped on 813 of 824 logged curriculum
and anneal steps, while the craft's raw norm never reached the clip (0 of 824;
max 0.62). The anneal is a DIET change (1B of prose, then 1B with the chat
frame), not a learning-rate decay: train/lr_scale read 1.000 first to last
on both crafts. Bank dependence at the end: control toggle_bank_off 6.11 vs
the craft's 3.31; the control's born-null aleph head stayed buried at every
row. Grade: an INSTANCE at one craft per arm; the protocol completed unaltered.
The claim that SDPA is not an adequate representative of this model's attention
requirement is AbstractPhil's (2026-09-02); its audit (2026-09-04) minted the
specific form — under this recipe (Muon 2e-2 / Adam 3e-4 flat, clip 1.0, no
QK-norm, no weight decay, no LR decay) the softmax twin is a destabilized
control and the endpoint gap measures stability, not capacity — and holds the
general form as a CANDIDATE behind five confounds: the recipe (the documented
softmax logit-growth instability, fixed in the literature by a normalization or
a decay), one seed with 20.3M extra parameters in the losing arm, the shape
(mini-beatrix-1, thirteen softmax blocks and three hubs, trained clean under the
identical configuration: post-warmup grad-norm max 0.88, no clipped step), the
aleph-addressed expert banks present in both crafts (the control's dispatch
entropy collapsed before its gradients did), and the learned position table on
decay-free Muon. Provenance: session caps at 22,854 and 53,792
(reports/v2/session_cap_step*.json) — the first resume landed on a rare H100
(s0–s8 rows), a third session from the 56,000 checkpoint ran the two anneal
rows on another card; the escalation began before any resume. Fifteen reports
under mini-beatrix-2s-control/reports/v2/ pair with the craft's by stage and
step; the paired chart is article_assets_ft2/01_control_crossover.png.mini-beatrix-2s/reports/v2/. Shipped
automodel: mini-beatrix-2s.
Notable ops in the record: the first BOUNDARY-WRITE head revival
(reports/v2/revival_step24860.json) and the archived pre-specials run0
(mini-beatrix-2s/archive/run0-nospecials/).| craft | d / L / ctx | params | tokenizer | role |
|---|---|---|---|---|
| mini-beatrix-0 | 512 / 12 / 1024 | 37.6M | byte-trigram | gate craft — its first toggle evals are the anchored-bank-under-AR screen |
| mini-beatrix-1 | 768 / 16 / 2048 | 112.5M | byte-trigram | first Colab mission |
| mini-beatrix-2 | 1024 / 20 / 2048 | 249.1M | byte-trigram | second mission |
| beatrix-voyager | 1536 / 24 / 4096 | 775.3M | BPE (gpt2) | flagship, gated on the mini verdicts |
*-control twin (identical minus the aleph attention
layers) — the running architecture control. For the 2s rung the twin is
mini-beatrix-2s-control (the same preset with hub_layers: [], so 16-head
causal SDPA in every block; 257.4M vs the craft's 237.1M), run to the same
16.101B-token endpoint under a pre-registered protocol — see the mission log;
its paired boundary reports sit under mini-beatrix-2s-control/reports/v2/.
Training is resume-first:
sessions are manually triggered on Colab (by default the RTX 6000 Pro, 96GB, bf16), each
session pulls manifest.json + resume/latest.pt and continues where the
last one stopped.hub_layers: [] in its manifest). See the mission log
and the model card of mini-beatrix-2s.