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Характеристики

Лицензия
Apache 2.0
Библиотека
transformers
Опубликована
26 сен 2026

amx-moe-eda-w2048-bugfixinit

Генерация текстаТекст → текст3 профессии

Экспериментальная MoE-модель для генерации функций на C в узком корпусе EDA-кода и изучения обучения малых моделей на CPU.

513скачиваний за 30 дней0лайковвеса обновлены28 сен 2026

Бенчмарки

Как модель справилась с задачами пользователей ailist и что о ней публикуют открытые лидерборды

Публичные оценки

Из открытых лидербордов. Сравнивайте результаты одного теста при одинаковых условиях — отсутствие оценки не значит, что модель хуже.

В подключённых источниках результатов для этой модели пока нет.

Что умеет модель

Описание составлено по карточке модели и метаданным Hugging Face

gdiamos выпустил экспериментальную 30-миллионную модель block-routed MoE с 3,3 млн активных параметров на токен для генерации функций на языке C в узком корпусе инструментов автоматизации проектирования электроники (EDA). Модель дообучена на программе исправления ошибок в реальном C-коде; использует окно внимания 2048 токенов и была обучена на одном ядре Intel AMX с библиотекой Transformers.

Модель предназначена для исследования обучения небольших MoE-моделей на CPU и генерации C-кода в диалекте EDA-корпуса и среды выполнения nrl. На отложенных блоках она показала снижение cross-entropy относительно прежней инициализации, однако преимущество по частоте компиляции не подтверждено. Лицензия — Apache-2.0.

Это исследовательский артефакт, а не помощник для разработки: точное совпадение на отложенных блоках составило 0%, а многие компилирующиеся результаты являются пустыми заглушками. Выводы следует проверять на соответствие спецификации и полезность, особенно за пределами обучающего корпуса.

Сценарии применения

  • Изучение генерации C-кода небольшой MoE-моделью, обученной на CPU
  • Эксперименты с генерацией функций для инструментов EDA
  • Сравнение влияния окна внимания и инициализации на языковую модель кода

Ограничения

На отложенных блоках точное совпадение составило 0%; компиляция не подтверждает правильность, и часть результатов является пустыми заглушками. Модель обучена на узком EDA-корпусе и диалекте среды nrl, поэтому не подходит для общего программирования на C.

Для каких профессий подходит3

Разделы каталога профессий, в задачах которых используется эта модель

Все профессии

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

amx-moe-eda-w2048-bugfixinit

A 30M-parameter block-routed MoE (3.3M active per token) fine-tuned to write C functions for a small EDA-tooling corpus. Trained entirely on one Intel AMX CPU core.

This release differs from amx-moe-eda-p250x4 in two ways, both measured rather than assumed:

  1. Attention window 2048 instead of 256.
  2. Initialised from a bug-fix curriculum on real C rather than from the base pretrain.

What it can and cannot do

Read this before using it. On 198 held-out EDA blocks -- programs it never trained on -- it reaches 1.2269 nats and 0% exact match. Of 61 held-out blocks compiled after substitution into the real program, 20.5% compile and 12.8% both compile and reference anything outside their own signature. It does not write correct C from a specification. It is a research artifact for studying what a CPU-trained model of this size does, not a coding assistant.

A note on the compile numbers

Two conventions appear below, and each table says which it uses.

The nrl_main block has a median target of 1,107 tokens against a 320-token generation cap, so every one of its generations is truncated by the harness before it can finish. Those score 0% by construction rather than by failure, and they are 36% of the held-out set.

  • Excluding nrl_main: 20.5% compile, 12.8% work. What the model does on work it can actually complete. Used in the summary above.
  • Including it: 13.1% compile, 8.2% work. Used in the comparison with the previous release, because that release's numbers were measured the same way and changing one side only would be misleading.

Any comparison with other numbers from this project should check which convention they used.

Measured results

Held-out EDA blocks, 198 targets, each arm at its own best checkpoint:

initwindowheld-out CE
4-day base2561.3358
4-day base20481.3054
bug-fix curriculum20481.2269

Paired against the 4-day init at window 2048, on identical blocks:

-0.0443 nats, t = -3.02, significant; wins on 122/198 blocks (62%)

Compile rate on 61 held-out blocks, against the 4-day init:

4-day initthis modelpaired p
compiles8.2%13.1%0.508
compiles and is not a stub or hollow6.6%8.2%1.000
hollow6.6%14.8%0.125

The compile advantage is not established, and its extra compiles are disproportionately hollow -- bodies that reference nothing outside their own signature. The cross-entropy gain is the real one.

Comparison with the previous release

The prior model is gdiamos/amx-moe-eda-p250x4. Both are the same architecture and the same 250-program corpus; this one adds window 2048 and the bug-fix init.

The two releases were selected by different criteria, and that matters more than the architecture difference. p250x4 published its step-20,631 checkpoint deliberately -- its card calls it "the fitted end of that trade", chosen because fit buys compile rate even as held-out loss gets worse. This release publishes step 4,125, chosen by held-out cross-entropy. So the honest comparison is two tables, not one.

As published: what you get if you download each

Both rows INCLUDE nrl_main, for parity with the previous release.

All numbers re-measured here on the same 61-block held-out set, so they are comparable to each other (they differ slightly from the p250x4 card, which used n=67 and its own eval run).

p250x4 (step 20,631)this model (step 4,125)
held-out cross-entropy1.68921.2269
blocks that compile8.2%13.1%
compile and not stub/hollow6.6%8.2%
hollow1.6%14.8%
exact match0%0%

Better on loss and on raw compile rate, and noticeably worse on hollow output -- nearly one in seven of this model's held-out generations compiles while referencing nothing outside its own signature, against one in sixty for p250x4. If you are counting compiles as successes without the guards, this model will flatter itself more than the old one did.

Controlled: both at step 4,125, same eval

Both rows INCLUDE nrl_main.

Strip out the checkpoint-selection difference and the picture narrows:

p250x4 armthis model
held-out cross-entropy1.33581.2269
blocks that compile14.8%13.1%
compile and not stub/hollow9.8%8.2%

Matched checkpoint for checkpoint, the gain is cross-entropy only. On compile rate the older configuration is nominally ahead, and a paired test over the same blocks puts the difference at p=1.000 -- i.e. no difference either way. Neither window 2048 nor the bug-fix init is shown to produce more compilable C on this task.

What is actually established

  • Cross-entropy: −0.0443 nats against the same-architecture 4-day init, t = −3.02, winning on 122/198 blocks. Significant and broad.
  • Compile rate: nothing. Six paired tests across these arms, none reaching p < 0.05 at n≈61 with 5–13 events per arm.
  • The reason the window helps less than its mechanism suggests: only 34.5% of EDA target lines appear verbatim in the prompt, against 96% on the bug-fix task. Retrieval pays only where there is something to retrieve.

Why the window is 2048

The copy circuit in this model family is hard-capped at the training window. Asked to repeat a literal span, the window-256 model scores 74.9% token accuracy at distance 259 and 0.2% at 367 -- a step function, not a decay. Held-out EDA block prompts have a median length of 882 tokens, so at window 256 roughly 72% of the skeleton a block must copy identifiers from was outside the model's reach.

Widening it helps less here than the mechanism suggests, because only 34.5% of EDA target lines appear verbatim in the prompt (against 96% on the bug-fix task). Retrieval only pays where there is something to retrieve.

Why a bug-fix init

The bug-fix curriculum -- 24,539 distinct C functions with one injected bug each -- was built to install a copy circuit and failed at that goal: the copy probe was unchanged at every checkpoint. It still transfers usefully here, most likely as 15k functions of real C rather than as a circuit.

Training

parameters30,029,429 total / 3,315,744 active per token
architecture6 layers, [lin, swa, swa, lin, swa, lin], d_model 256
MoE64 experts, k=4, 2 shared, route_block 256
window2048, chunked
hardwareone Intel AMX CPU core
initbug-fix window-2048 model, itself from a 1.75B-token pretrain
fine-tune84.5M tokens; best checkpoint is step 4,125, not the final

The released checkpoint is step 4,125. The final step 20,631 checkpoint scores 1.5251 -- 24% worse -- because 200 passes over 250 programs is far past the optimum. Anyone reproducing this should early-stop.

Known limitations

  • 0% exact match on held-out blocks. Generation derails over long outputs: per-token accuracy on copyable spans is about 52%, which compounds.
  • Hollow outputs. 14.8% of held-out generations compile while computing nothing. Check outputs against the stub/hollow guards before counting a compile as a success.
  • Trained on one narrow corpus. It speaks this EDA dialect and its nrl runtime, not C in general.
  • Greedy decoding derails; sampling and repetition penalties were swept and none produced a significant improvement.

License

Apache 2.0.

Как запустить

На своей GPU в ailist. Цена — по текущим предложениям провайдеров

GPU в ailist

Универсальный рантайм · transformers

Нельзя запустить

Архитектура ? неизвестна transformers в образе рантайма.

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