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Decision 3.0 now on Core ML 🍎 d3-lite triaging 1,000 pull requests into vllm-sr workgroups, live on an M5 Pro: 0.12 s per PR, 8.3 PRs/s, nothing leaves the Mac ⚡ lite, nano and mini are all converted and in one repo 👇 🤗 🛠️

28,274 просмотров • 21 дней назад •via X (Twitter)

Комментарии: 15

Фото профиля basedcapital
basedcapital21 дней назад

a thousand prs triaged at 8.3 a second on a mac, by a model built for routing decisions. the boring infra jobs are where small models win

Фото профиля Wallet Watcher
Wallet Watcher20 дней назад

d3-lite at 0.12s per PR is the number that matters. everything local, nothing leaves the Mac. that's the part people skip when they benchmark against API calls.

Фото профиля Rajan Singh
Rajan Singh19 дней назад

Great work man

Фото профиля NYC Arb Scanner
NYC Arb Scanner20 дней назад

0.12s per PR on-device, nothing leaving the Mac. that's the number I care about — local cadence with no network tail is the whole point.

Фото профиля Julian · Media AI
Julian · Media AI21 дней назад

A 0.6B model that watches video and just picks an answer is the tiny intern of my dreams 🎬

Фото профиля Darui
Darui21 дней назад

When a PR is ambiguous between workgroups, can the router leave it in a human-review queue, or does it always have to pick one?

Фото профиля Alex
Alex21 дней назад

in general decision models should have that option but for this demo the pre existing sections is suffice for demoing.

Фото профиля Muhamed Fazal PS
Muhamed Fazal PS21 дней назад

sub-120ms per pr locally on core ml without hitting cloud tokens is clean. are you running direct ane fp16 execution for the embedding classification passes, or falling back to gpu metal shaders on the larger token context windows?

Фото профиля Alex
Alex19 дней назад

All GPU: Core ML on the Metal GPU, int8 weights with fp16 compute, padded to a few fixed prompt lengths. d3's hybrid DeltaNet layers don't compile for the Neural Engine yet, so Core ML falls back to CPU there and the GPU is the fast path. 0.12 s per PR = 2 questions on d3-lite, ~50 ms each.

Фото профиля Muhamed Fazal PS
Muhamed Fazal PS19 дней назад

metal gpu with fp16 accumulators avoids the neural engine dynamic shape penalty anyway. fixed prompt bucket padding is the move. stops coreml graph recompilation on variable token lengths.

Фото профиля BreezeOg
BreezeOg21 дней назад

0.12s per pr on a laptop and nothing leaves the mac, that's the part i like

Фото профиля T.Rex-Baby
T.Rex-Baby21 дней назад

Not bad, still a long way to go for full usage. Fast but not fast enough for heavy continuous work.

Фото профиля icreate
icreate21 дней назад

@grok any jev benchmark on this one?

Фото профиля Felipe Daragon
Felipe Daragon21 дней назад

how is D3 comparing to Laya?

Фото профиля Alex
Alex21 дней назад

i think they serve different purposes. i haven't fully explored laya but d3 does seem to offer way more options in terms of tasks, quality and inference options

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