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Hello Japan! 🇯🇵 Want to join a 2-day in-person hackathon co-hosted by Liquid AI and @weights_biases? Developers and AI researchers from around the world will gather to help shape the future of AI in Japan! This hackathon’s theme is “Push SLM models to the limit.” The selected…
Let's build an image classification app with local Visual Language Models ⬇️ 𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 🤔 After years of seeing Visual Language Models confined to expensive cloud APIs and GPT-4V monopolies, I decided to prove a point: >> VLMs can run efficiently on…
small but mighty
Today we are releasing a series of small task specific models that can deliver high quality task (data extraction, translation, RAG, tool use, or math) performance in a very efficient package. Huggingface: huggingface.co/collections/Li…
Today we are releasing a series of small task specific models that can deliver high quality task (data extraction, translation, RAG, tool use, or math) performance in a very efficient package. Huggingface: huggingface.co/collections/Li…
Introducing Liquid Nanos ⚛️ — a new family of extremely tiny task-specific models that deliver GPT-4o-class performance while running directly on phones, laptops, cars, embedded devices, and GPUs with the lowest latency and fastest generation speed. > model size: 350M to 2.6B >…
What if we've been doing agentic AI all wrong? Meet Liquid AI's Liquid Nanos venturebeat.com/ai/what-if-wev…
pushing usable, high quality inference to the edge is the next phase shift models like this that deliver GPT-4o performance in some cases get us closer to that point great work and results by @ramin_m_h and team! #investor
pushing usable, high quality inference to the edge is the next phase shift models like this that deliver GPT-4o performance in some cases get us closer to that point great work and results by @ramin_m_h and team! #investor
By 2027, ~$1.1T to ~$1.6T capital is planned to be deployed on data centers! while this is exciting and important, the economics needs a close look. with today’s ~30-40% AI growth margins, those dollars get paid back by 2033, only if efficiency keeps compounding, AI’s get…
By 2027, ~$1.1T to ~$1.6T capital is planned to be deployed on data centers! while this is exciting and important, the economics needs a close look. with today’s ~30-40% AI growth margins, those dollars get paid back by 2033, only if efficiency keeps compounding, AI’s get…
Liquid AI debuts extremely small, high-performance foundation models for on device ift.tt/O9jSl0T
We're releasing a collection of tiny task-specific models 🥳 Want to do data extraction, translation, RAG, tool use, or math on a Raspberry Pi? We got you covered! ✅ Here are a few examples ↓
We continue to scale our @LiquidAI_ LFM2 series of super efficient language models with LFM2-2.6B
This is something small, with many babies coming this week! ⚛️
Meet LFM2-2.6B, the newest member of our LFM2 family, a new leader in the 3B model class. > light-weight with 2.6B parameters > fast, built by our v2 efficient architecture (short convs + group query attention) > Trained on 10T tokens32k context length > open-weight,…
Beautiful work of Keshi and our team on grafting diffusion transformers now accepted for Oral presentation at Neurips! #NeurIPS2025
Beautiful work of Keshi and our team on grafting diffusion transformers now accepted for Oral presentation at Neurips! #NeurIPS2025
Grafting Diffusion Transformers accepted to #NeurIPS2025 as an Oral! We have lots of interesting analysis, a test bed for model grafting, and insights🚀 📄Paper: arxiv.org/abs/2506.05340… 🌎Website: grafting.stanford.edu
Grafting Diffusion Transformers accepted to #NeurIPS2025 as an Oral! We have lots of interesting analysis, a test bed for model grafting, and insights🚀 📄Paper: arxiv.org/abs/2506.05340… 🌎Website: grafting.stanford.edu
turns out I placed 1st in this @LiquidAI_ competition !! I'm just so thankful for everyone that's supporting us , shoutout to the homies @nisten @apaz_cli @AlpinDale @abidlabs all the folks at @huggingface , @lium_io for calling it early @runpod_io for sponsoring some runs
turns out I placed 1st in this @LiquidAI_ competition !! I'm just so thankful for everyone that's supporting us , shoutout to the homies @nisten @apaz_cli @AlpinDale @abidlabs all the folks at @huggingface , @lium_io for calling it early @runpod_io for sponsoring some runs
We trained our @LiquidAI_ LFM2-350M model 1400x beyond "compute optimal" > Chinchilla scaling laws: ~20 tokens per param > LFM2-350M: ~28,000 tokens per param (1400x more) Why? Because Chinchilla only concerns training compute, while we care about inference cost

MIT CSAIL @MIT_CSAIL
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