Rich Counts

Financial services: 8k in → 64 out; LFM2.5-350M on B200. During a credit application, the model evaluates applicant data alongside financial and risk signals and returns a structured eligibility or credit decision.

Today, we announce 𝙻𝚒𝚚𝚞𝚒𝚍 𝚁𝙸𝙿𝙻 (real-time inference pipeline), our ultra low-latency inference engine built to put LFMs directly in the request path of real-time workloads like search, ads, fraud, recommendations, and payments. > complete generated responses end-to-end in sub 50ms > maintains low latency under continuous peak load > LFMs get up to 20x faster on RIPL > RIPL supports Multimodal LFMs > RIPL supports d1, our decision models too! RIPL optimizes the full path from request arrival to a complete generated response. As traffic rises, its performance advantage is greatest on the slowest requests. Let’s see some production use cases:

Today we release 𝚍𝟷-𝚞𝚕𝚝𝚛𝚊, our most powerful decision model. > built for hard decision problems. > d1-ultra is multimodal > supports 44 languages > #1 on the @ArtificialAnlys Decision Index (65) > leads all eight categories of our 112-evaluation suite 1/

We're partnering with @Qualcomm to bring personal AI context to devices powered by Snapdragon. Liquid Context, our on-device context layer, is now optimized for Snapdragon processors and runs on the Qualcomm Hexagon NPU. Our goal: Give the agents people choose an understanding of what matters to them and when they need help. With the user's permission, Liquid Context learns from device signals and builds an understanding of their routines, preferences, and needs. That understanding is built and maintained locally, and Liquid Context shares relevant context with the user's chosen agents, whether they run on the device, in the cloud, or across both. That includes third-party agents and Liquid Agent, our efficient embedded agent powered by LFM2.5-2.6B. Running on the Hexagon NPU, Liquid Context works in the background and keeps that understanding current without requiring a cloud model to process every update. For device manufacturers, this is a path to add personal context to their devices while supporting their own choice of agents and services. OEMs building embedded or hybrid agents can also work with us to evaluate Liquid Agent. As our CEO @ramin_m_h said: "Personal AI starts with understanding how you live and what you need, when you need it. Liquid Context builds that understanding on your device so the agents you choose can offer more relevant help and anticipate your needs." > Read more about our partnership: liquid.ai/blog/liquid-contex… > Check out the livestream of @cristianoamon and Ramin's keynote here: youtube.com/watch?v=xKCto1Yf…

Aging research spans many biological modalities, each traditionally requiring specialized models and pipelines. Instead, we train a single foundation model as a common reasoning layer across them – opening the door for researchers to interrogate clinical, genetic, epigenetic, transcriptomic, and proteomic data with the same model: > 🏥 Clinical: LFM2-2.6B ranked #1 on NHANES pairwise age prediction > 🧬 Genetics: LFM2-1.2B and 2.6B ranked #1 and #2 on masked OpenGenes. > 🧪 Methylation: LFM2-2.6B ranked #2 overall on GEO pairwise age. > 📊 Transcriptomics: LFM2-2.6B ranked #2 overall on GTEx age classification. > 🔬 Proteomics: both LFMs beat every frontier model evaluated on Olink pairwise age. We also ran feature ablations to test which biological measurements influence model predictions. Removing selected feature groups reduced confidence in correct answers and, in some cases, changed the final prediction. Our results demonstrate that LFMs drew on task-specific biological information rather than a single common shortcut.

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. 🧵

In strong support of open-weight AI and American AI leadership, we are proud contributors to the open-source community and excited to announce that Liquid Foundation Models (LFMs) have surpassed 40 million downloads by the community! Going forward, we remain committed to accelerating the open-weight release of the next generation of lightweight, powerful LFMs to the world. excited to see what you build with them! liquid.ai/models – at San Francisco, CA

We doubled LFM2.5-8B-A1B's tokenizer from 65K to 128K to fix the languages it split too finely. Today we're sharing the recipe for upgrading a pretrained model's tokenizer in place. > Thai now takes 4.0× fewer tokens, Vietnamese 2.6×, Hindi 2.4× > Est. 2.2 to 3.7× faster per-character decoding on-device for these languages 🧵

Introducing LFM2.5-230M: our smallest model yet, built to run fast anywhere (CPUs, NPUs, and GPUs) to enable agentic tasks on phones, robots, home and network automation devices. > 230M parameters, built on the LFM2 architecture > Pre-trained on 19T tokens, with a 32K context extension > Post-trained with distillation from LFM2.5-350M > 213 tok/s decode speed on Galaxy S25 Ultra (CPU) > 42 tok/s on a Raspberry Pi 5 (CPU) > Competes with and often beats models more than twice its size on instruction following, data extraction, and tool use. > use it for large-scale data extraction pipelines or lightweight on-device agentic workloads. 🧵