Alex

@Alex_tra_memory • 1,168 subscribers

@fluidinference , AI Researcher https://t.co/8ko2FKWp4A

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Anya Rossi

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Thanks for the model, we were able to port Laya to coreml with 99.5% of the ops on ANE + benchmarked too. it is now blazing fast with 3.7 ms per decision on an M5 Pro. Release: Models:

Thanks for the model, we were able to port Laya to coreml with 99.5% of the ops on ANE + benchmarked too. it is now blazing fast with 3.7 ms per decision on an M5 Pro. Release: Models:

120,068 次观看 • 1 个月前

LFM3-1.7B just read the whole of Pride and Prejudice on a M5 Pro using MLX. It read 100k tokens in about 28 s (sped up 8× in the clip), then answered 100 questions back to back in 27 s, including finding a password hidden somewhere in the book.

LFM3-1.7B just read the whole of Pride and Prejudice on a M5 Pro using MLX. It read 100k tokens in about 28 s (sped up 8× in the clip), then answered 100 questions back to back in 27 s, including finding a password hidden somewhere in the book.

14,620 次观看 • 4 天前

We got EmbeddingGemma 2 running 100% on the Apple Neural Engine 🍎 M5 Pro 10,080 posts sorted into topics live, ~620/s on a MacBook, then any topic splits into subtopics in under a second. Fully on-device. 🤗 💻

We got EmbeddingGemma 2 running 100% on the Apple Neural Engine 🍎 M5 Pro 10,080 posts sorted into topics live, ~620/s on a MacBook, then any topic splits into subtopics in under a second. Fully on-device. 🤗 💻

40,731 次观看 • 24 天前

We distilled clef-flash into a 0.6B model and supporting ANE option. 1,000 support tickets triaged on a M5 pro in 57 s: team, urgency and refund for each, 48 ms per ticket. Within a few points of the 9B on classification. 🤗 💻

We distilled clef-flash into a 0.6B model and supporting ANE option. 1,000 support tickets triaged on a M5 pro in 57 s: team, urgency and refund for each, 48 ms per ticket. Within a few points of the 9B on classification. 🤗 💻

32,227 次观看 • 23 天前

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 👇 🤗 🛠️

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 次观看 • 21 天前

Inspired by Shahir's demo, FluidAudio Phonon 2 coreml transcribing an hour of audio in 5.6 s and a full 24 hours in 2 min 19 s on a M5 Pro. Phonon-2 Core ML 1 hour rtfx went from 480× → 644×. test-clean wer the same while 150 -> 195 rtfx

Inspired by Shahir's demo, FluidAudio Phonon 2 coreml transcribing an hour of audio in 5.6 s and a full 24 hours in 2 min 19 s on a M5 Pro. Phonon-2 Core ML 1 hour rtfx went from 480× → 644×. test-clean wer the same while 150 -> 195 rtfx

14,278 次观看 • 20 天前

finetuned Unlimited-OCR on a M5 Pro, Baidu's whole 14-page paper in Core ML reads the pages, MLX writes ~200 tokens/s.

finetuned Unlimited-OCR on a M5 Pro, Baidu's whole 14-page paper in Core ML reads the pages, MLX writes ~200 tokens/s.

12,459 次观看 • 20 天前

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