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Introducing VisionPsy-Nano: state-of-the-art vision-language models at 460M parameters, small enough to run on the phone in your pocket. VisionPsy-Nano-460M leads every other ~0.5B vision-language model tested & compares favourably on 16 of 17 benchmarks, tops all four capability categories, and outperforms models up to 2.3x larger. Two variants, one... show more

QVAC

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15,329,496 views • 1 month ago •via X (Twitter)

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QVAC1 month ago

Everything is out today: open weights, the blog, and the full benchmark breakdown. Model weights: Hugging Face Blog :

adidshaft1 month ago

does the 0.3s iPhone TTFT include image decode and resize, or does timing start once the 512x512 tensor is ready?

just soph1 month ago

wait can i run this on my iphone?

Ryan White1 month ago

Open weights, runs on a phone, beats models 2.3x its size. Brussels still drafting rules for models that won't exist by the time they finish.

AI Mastery Guide1 month ago

460M beating models 2x its size is genuinely impressive

smiks1 month ago

@woxshter le goat

med halbaj1 month ago

Gibraltar est européenne mais Ceuta et Melilla sont africaine

sophs b.1 month ago

how's it compare to the phi-3 vision models at similar size?

DeFiDave1 month ago

Wow

Rompel1 month ago

460M leading its class on-device is real. But "leads every benchmark" self-scored needs a number. The story is decode tok/s + prefill on actual phone silicon—and the SigLIP encoder cost, since the vision tower usually eats mobile latency, not the 460M LM. What's real-time in ms?

HiddenGuardian1 month ago

this is a pretty interesting pivot from running a node to shipping a phone-sized vision model

Alec Zakhary1 month ago

The 460M number is interesting; the real product win isn't a benchmark. Do the privacy- and latency-sensitive first pass on-device, then escalate only ambiguous cases. For food recognition, a tiny local model that knows when it's unsure can beat a bigger cloud model in UX.

Leo Lin1 month ago

A capable vision-language model running on the phone in your pocket is the quiet shift I keep watching. Intelligence moving to the edge changes what a device — or a robot — can decide on its own, without a round trip to the cloud.

Elara AI1 month ago

460M on device and beating 2.3x larger models is insane

Nexzil Labs1 month ago

Impressive work! A 460M parameter VLM outperforming models 2.3x larger is a great testament to efficient architecture design. Apache 2.0 licensing and on-device capability make this really practical for real-world AI applications.

OBDient1 month ago

Great news!

AfterHourWhaleLon1 month ago

that caught my attention

bolaji1 month ago

Nice

Sebastian Buzdugan1 month ago

460m is great on phone until sustained camera use hits thermals and latency

王小庄1 month ago

Honestly I'd trade a few benchmark points for stable latency once the phone's been running the camera for a few minutes

mcbusta1 month ago

Bla bla bla...bull shit....

DiamondBobmac💎1 month ago

Active

Aria Tech1 month ago

phone sized vision model beating bigger ones is wild

0xAlex1 month ago

The height of technology keeps increasing by the day

Vantix AI Agency1 month ago

VisionPsy Nano 460M runs on your phone and tops benchmarks

ZenithAi1 month ago

Impressive vision performance packed into such a compact model

Drake1 month ago

Zbb e7 8ef

hanqing1 month ago

Tether终于不务正业了？不过VisionPsy要是能用USDT训练，我第一个下单——毕竟币圈最不缺的就是算力🔥

TKdesigner 🎨👑1 month ago

Local AI running directly on phones is a huge win 💪

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