LFM2.5 Retrievers: Bi-directional LFMs for Fast Multilingual Search | Liquid AI
Discover LFM2.5 Retrievers, 350M-parameter multilingual embedding and ColBERT models for fast, accurate cross-lingual search across 11 languages.
Discover LFM2.5 Retrievers, 350M-parameter multilingual embedding and ColBERT models for fast, accurate cross-lingual search across 11 languages.
Today, we’re releasing LFM2.5-8B-A1B, a high-throughput edge model optimized for fast, reliable tool calling and complex instruction following on consumer hardware, delivering compressed performance competitive with…
Today, we release LFM2.5-VL-450M, an improved version of LFM2-VL-450M with grounding capabilities, better instruction following, and function calling support. The result is a compact model that…
Today, we're releasing LFM2.5-350M, an improved version of our 350M model with additional pre-training (from 10T to 28T tokens) and large-scale reinforcement learning. Built on the…
Building a local AI agent sounds great until you try to use one all day. The hard part isn’t getting a model to understand you, it’s…
Today, we are completing the LFM2 family with the launch of our most capable model yet: LFM2-24B-A2B. While our Technical Blog dives into the architectural specs,…
Today, we release an early checkpoint of LFM2-24B-A2B, our largest LFM2 model. This sparse Mixture of Experts (MoE) model has 24 billion total parameters with 2…
Today, we are releasing LFM2.5-1.2B-Thinking, a reasoning model that runs entirely on-device. It fits within 900 MB of memory on a phone and delivers both the…
For the last few years, the AI narrative has been dominated by a 'bigger is better' philosophy. We’ve watched parameters balloon into hundreds of billions or…
This week at CES, our Liquid AI team and partners at AMD are showcasing the future of on-device intelligence with lightning fast, reliable and entirely secure…