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Adding vision to a language model is more than one extra modality. With large images or long video, the vision encoder can use more compute than the language model itself, so efficient models need a different design. Our VLM team, including ryan, Tianshu Yu, Sina Semnani, and Sam Stevens,... show more

Liquid AI

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11,633 görüntüleme • 2 gün önce •via X (Twitter)

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Joshua Timothy profil fotoğrafı

Joshua Timothy2 gün önce

@rshubert98 @TianshuYu6 This VLM is so good!

luis profil fotoğrafı

luis2 gün önce

@rshubert98 @TianshuYu6 @Presidentlin

Humayun profil fotoğrafı

Humayun2 gün önce

@rshubert98 @TianshuYu6 ran d1 on a plain cpu box and loved it, vision next please

Majsans bästa kusin profil fotoğrafı

Majsans bästa kusin1 gün önce

@rshubert98 @TianshuYu6 You guys are a gang to watch

K4Rambo profil fotoğrafı

K4Rambo2 gün önce

@rshubert98 @TianshuYu6 The Liquid team did a really good job. The model's capability far exceeds what previous 1.7B and 2B vision models could do. Huge success! You guys are on the frontier of making Artificial Intelligence free for everyone.

Rithesh profil fotoğrafı

Rithesh1 gün önce

@rshubert98 @TianshuYu6 Tested and created this video

Elyria l4 profil fotoğrafı

Elyria l42 gün önce

@rshubert98 @TianshuYu6 @grok so does this latest lfm3 latest vision encoder can stream higher than 30fps or real time video understanding and able to identify events in industrial applications than general vlms ? General vlms down sample native rate and lose context and how come this can be different?

Veera Tech info profil fotoğrafı

Veera Tech info2 gün önce

@rshubert98 @TianshuYu6 Teaching AI to truly see smarter, faster vision for the next generation!

Synapse profil fotoğrafı

Synapse2 gün önce

@rshubert98 @TianshuYu6 Wow, as someone in the field, I can say @liquidai is gonna be a huge protagonist in the future of AI.

Pratik Gandhi profil fotoğrafı

Pratik Gandhi2 gün önce

@rshubert98 @TianshuYu6 love this! how much of the compute does the encoder take on long video?

ReignSwords profil fotoğrafı

ReignSwords2 gün önce

@rshubert98 @TianshuYu6 The compute flip is the key insight — once the vision encoder eats more FLOPs than the LM, bolting on a standard ViT stops being free. Makes me wonder how much of LFM3's efficiency actually comes from the encoder design vs. the language backbone.

David Liu profil fotoğrafı

David Liu2 gün önce

@rshubert98 @TianshuYu6 🔥

JG 🇮🇪 profil fotoğrafı

JG 🇮🇪2 gün önce

@rshubert98 @TianshuYu6 Vision encoder outspending the LLM is the bit nobody budgets for. At what image size does that flip?

Ernesto Davila Tirado profil fotoğrafı

Ernesto Davila Tirado2 gün önce

@rshubert98 @TianshuYu6 goats

PELLUET Guillaume - Ph.D. profil fotoğrafı

PELLUET Guillaume - Ph.D.1 gün önce

@rshubert98 @TianshuYu6 Mid-training into a custom vision encoder is the move to watch. When vision tokens cost more than language, the mix has to change with resolution, not just with epoch. 🎛️

Dominik profil fotoğrafı

Dominik2 gün önce

@rshubert98 @TianshuYu6 an encoder that out-spends the language model on long video quietly changes how you size everything

jimerrors profil fotoğrafı

jimerrors2 gün önce

@rshubert98 @TianshuYu6 Mid-training to a custom encoder for LFM3 is the jump I'm watching — long video is where most bolted-on vision setups start to hurt.

HEISGIFT profil fotoğrafı

HEISGIFT2 gün önce

@rshubert98 @TianshuYu6 encoder out-costing the llm on long video

IronaX profil fotoğrafı

IronaX2 gün önce

@rshubert98 @TianshuYu6 We spent years moving everything to the cloud. Now the biggest flex is that nothing leaves your phone.

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