alephllm โ€” Nano-Beatrix training runs

The training ground for the nano line of Beatrix: byte-level crafts under ten million parameters, trained from birth to find out what a craft that small can hold, how it holds it, and how fast it trains. The crafts, the trainer and the model code live in the library github.com/AbstractEyes/alephllm (package geolip.alephllm); the bed that drives them lives in github.com/AbstractEyes/geolip-nano-beatrix-trainer. This repository holds what training produces. It is the small sibling of alephllm-mini-beatrix-training, where the 237M and 257M teachers, Virgil (the splat) and Matelda (the softmax control), were trained.

bed/manifest.json          every run: arm, seed, steps, context, parameters, held-out bits, the fetch, minutes, the weights path
bed/reports/<run>.json     the full record of one run: the training curve, the held-out bits by byte class and by position in the
                           window, the planted fetch at each distance, the expert dispatch census, the time cost
bed/reports/README.md      the results index: one table per context, built from the records
bed/weights/<run>/         model.safetensors + config.json (loadable with the library's AlephLM from the config)

The bed

Every run trains one small craft: six blocks of width 256, eight heads, a bank of seven experts of width 256 in every block, 7.7 to 9.7 million parameters, a byte vocabulary of 256. The text is fineweb-edu (the sample-10BT configuration, documents of 400 to 24,000 bytes packed back to back). Into the training rows the bed plants records: a sentence such as The secret code is 48213. and, some hundreds or thousands of bytes later, the same sentence again, whose value the craft can only get right by looking it up. The optimizer is plain Adam with a short warmup and a cosine schedule.

At the end of a run the bed reads, on held-out documents: the bits a byte (overall, by byte class, and by position in the window, in bands from the start of the document); the planted fetch at several distances (the teacher-forced bits of the value, the greedy first byte, the whole value copied); the expert dispatch census by byte class; and the time cost (minutes, tokens a second).

The arms are an attention form times a bank form:

attention form what it is
softmax, learned positions ordinary attention with a learned position table
softmax, rotary ordinary attention with rotary positions
hub every block a splat hub: a scan over the row with a signed address of a few anchors a book, the position carried by a phase on the codes when rotary is on
dual hub the hub with two address masses a block
alternating hub blocks and softmax blocks taking turns, rotary on one half or on both
bank form what it is
free the experts chosen by the aleph address alone
fixed byte-class table an installed table routes each byte class to its own expert; the learned dispatch is gone
both the address and the table together

What the first night showed (2026-10-12)

All runs below are from birth at a 4,096-byte window, 12,000 steps of 8 rows, five planted records a row, unless stated. The card was a shared desktop 4090, so the minutes are an upper bound; the tokens a second are the run's own count.

form expert table parameters held-out bits a byte whole value copied at 64 / 512 / 1,024 / 2,048 / 3,072 bytes minutes tokens a second
softmax, learned positions learned 9.21M 1.547 0.97 / 1.00 / 0.95 / 0.97 / 0.97 45.6 144k
softmax, rotary, seed 1 learned 9.21M 1.535 0.97 / 0.86 / 0.80 / 0.97 / 0.98 19.2 341k
softmax, rotary, seed 2 learned 9.21M 1.516 0.98 / 0.97 / 0.88 / 0.86 / 0.84 29.9 220k
softmax, rotary fixed byte-class 9.21M 1.532 0.98 / 0.97 / 0.89 / 0.86 / 0.66 17.1 384k
alternating, rotary on the softmax half only learned 8.85M 1.576 0.83 / 0.58 / 0.16 / 0.06 / 0.00 14.7 -
alternating, rotary on both halves, seed 1 learned 8.85M 1.567 0.97 / 0.98 / 0.94 / 0.98 / 0.94 13.6 481k
alternating, rotary on both halves, seed 2 learned 8.85M 1.555 0.89 / 0.84 / 0.78 / 0.69 / 0.47 16.1 407k
alternating, rotary on both halves fixed byte-class 8.85M 1.555 0.97 / 0.95 / 0.86 / 0.75 / 0.72 14.4 455k

The softmax forms copy a planted value from three thousand bytes back from birth. The mixed craft does the same once the hub half carries the phase on its codes, at the fastest step of the set. The fixed byte-class table costs no text and no copy on either body and reads digits and capitals a few hundredths of a bit better.

The hub on its own needs pressure before it forms the lookup from birth at this window. The pressure is the number of positions each half-axis of the address must cover: 4,096 bytes divided by twice the anchors a book.

hub form (rotary on the codes) positions a half-axis parameters held-out bits first byte at 64 / 512 / 1,024 / 2,048 / 3,072 whole value minutes
64 anchors a book 32 8.65M 1.730 0.05 / 0.11 / 0.09 / 0.16 / 0.11 0 everywhere 42.3
128 anchors 16 8.67M 1.689 0.05 / 0.08 / 0.09 / 0.14 / 0.11 0 everywhere 21.7
256 anchors 8 8.72M 1.690 1.00 / 0.70 / 0.72 / 0.50 / 0.12 0.05 / 0 / 0 / 0 / 0 35.4
512 anchors 4 8.82M 1.658 0.95 / 0.95 / 0.98 / 1.00 / 0.97 0.59 / 0.66 / 0.56 / 0.66 / 0.52 52.0
four books of 64 32 8.87M 1.717 0.06 / 0.16 / 0.09 / 0.17 / 0.14 0 everywhere 22.1
eight books of 64 32 9.31M 1.721 0.16 / 0.14 / 0.09 / 0.11 / 0.11 0 everywhere 31.0
64 anchors, 24,000 steps in one schedule 32 8.65M 1.593 0.98 / 1.00 / 0.47 / 0.11 / 0.09 0.08 / 0.05 / 0 / 0 / 0 33.8
64 anchors, formed at a 1,024 window first, then 12,000 steps at 4,096 32 8.65M 1.681 0.89 / 0.89 / 0.94 / 0.95 / 0.66 0.62 / 0.66 / 0.72 / 0.50 / 0.50 17.9
two masses of 64, 12,000 steps and then 12,000 more 32 9.66M 1.572 1.00 everywhere 0.97 / 0.91 / 0.89 / 0.91 / 0.94 24.9 + 30.3

Three things follow. There is a threshold between 16 and 8 positions a half-axis, not a slope: below it the width keeps paying, and at 4 the whole value forms at every distance. Books add parameters, not width: four or eight books of 64 form nothing where one book of 512 forms everything. Two roads open the narrow address anyway: form the lookup at a short window and extend, or give the block two masses. Whether the second mass or the fresh learning-rate schedule did the forming in the last row is one control away and is queued.

The byte-class table at a 1,024 window (2,500 steps; the back-to-back timing on a free card gave 58.1 against 65.5 ms a step and 0.46 against 0.55 ms an inference row):

body expert table held-out bits digit bits
softmax, learned positions learned 1.889 4.06
softmax, learned positions fixed 1.892 3.99
softmax, rotary learned 1.792 4.00
softmax, rotary fixed 1.790 3.96

Bits by position in the window (held-out documents, bands in bytes from the document start):

craft at 4,096 0-64 64-256 256-1,024 1,024-2,048 2,048-4,096
softmax, rotary, seed 2 2.14 1.63 1.55 1.49 1.49
alternating, rotary on both halves, fixed table 2.15 1.67 1.57 1.53 1.54
two masses of 64, 24,000 steps 2.17 1.66 1.59 1.56 1.55

Ordinary text gets cheaper out to about a thousand or two thousand bytes and is flat past that at this size, while the planted lookup reaches three thousand bytes. The far half of a long window pays for memory, not for prose, in a craft this small.

What comes next

The frame beds: rows built from frames of generic sizes (32, 128, 256 bytes and up) opened and closed by the byte registry's own tags, nested and overlapping, with records to look up from the window or to recall from the weights; the capacity curves that say what the craft holds; the twin pair, Castor (the rotary softmax body) and Pollux (the all-rotary alternating body), both with the byte-class table beside the aleph address (the bank form two seeds settled), trained under the same recipe and read side by side. Castor is the mortal twin whose memory grows with the window; Pollux the immortal one whose hub half keeps a constant decode memory.

Licence and credit

The weights are CC BY-NC-SA 4.0 and the code MIT, as LICENSE.md states; the training text is fineweb-edu (ODC-By 1.0). Direction and design by AbstractPhil; the experiments, the bed and these records built in collaboration with Claude (Anthropic).

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