Liquid AI
@liquidai • 32,062 subscribers
Build efficient general-purpose AI at every scale.
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Today we release LFM2.5-Encoder-230M and LFM2.5-Encoder-350M: bidirectional encoders that stay fast at long context, even on CPU. > LFM2.5-Encoder-230M: about 3.7x faster than ModernBERT-base on CPU at 8,192 tokens. Under 30s per forward pass, versus over a minute and a half. > LFM2.5-Encoder-350M: 4th of 14 models on GLUE, SuperGLUE, and multilingual classification, behind only three larger models, one of them nearly 10x its size. 🧵
Liquid AI191,046 views • 3 days ago
Storing too many tools in your context window increases latency and can lead to wrong tool selection. In this demo, we used LFM2.5-ColBERT-350M as a filter to only select the five most relevant tools among 151 options. It's fast and reliable, even without any specific fine-tuning. Try the demo on Hugging Face! And learn more on our blog:
Liquid AI33,392 views • 1 month ago
Building a model is just the start. Post-training makes it useful. Our CTO Mathias Lechner (Mathias Lechner) sits down for a conversation with Maxime Labonne (Maxime Labonne), our head of post-training, on the pipeline that takes a base model from autocomplete to something that can reason and follow instructions.
Liquid AI26,044 views • 2 months ago
Meet Liquid ShieldFlow. An on-device privacy layer powered by a device-native Liquid Foundation Model, Liquid ShieldFlow redacts sensitive data before it ever leaves your machine. No GPU needed and light on memory. It runs on almost any PC, locally, in real time. ShieldFlow was featured yesterday at Microsoft Build for Foundry Local. It also ran live on AMD laptops at 𝐂𝐎𝐌𝐏𝐔𝐓𝐄𝐗 𝐓𝐀𝐈𝐏𝐄𝐈. Request your early access here to ShieldFlow here:
Liquid AI20,762 views • 1 month ago
The bottleneck in LLM inference isn't compute. It's how fast you can move the weights. Our CTO Mathias Lechner, Mathias Lechner, joins Piotr Mazurek, Piotr Mazurek (in SF 🌉), from our inference team, to discuss what actually limits token throughput and how we're optimizing for it.
Liquid AI21,393 views • 2 months ago
Training LFMs at scale means solving parallelism across every layer of the architecture. And not all layers are the same. Our CTO Mathias Lechner (Mathias Lechner) sits down with Liquid's founding engineer Paul Pak (Paul Pak) to talk training infrastructure: Data, tensor, pipeline, expert, and context parallelism, and how they make context parallelism work across hybrid architectures with both attention and convolution operators.
Liquid AI17,908 views • 1 month ago
Most multimodal systems need data that combines every modality together. Hard to get, expensive to build. Our CTO Mathias Lechner, Mathias Lechner, sits down with Saniya Karwa, Saniya Karwa, from our multimodal research team to talk about building a mode that handles text, audio, and image, and why you might not need as much combined training data as you think.
Liquid AI15,129 views • 1 month ago
Well said. AI shouldn't just live in a data center, but it should run on the device you're already using: Whether it's a phone, a car, an appliance, or another everyday device in the real world. That’s what we’re working on at Liquid AI. We appreciate the spotlight on our COO Jeffrey Li (jeffrey li), who joined the AI-Curious Podcast (AI-Curious). Thank you, Jeff Wilser!
Liquid AI12,995 views • 2 months ago
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