LFM2 Technical Report
Authors: name: Alexander Amini, name: Anna Banaszak, name: Harold Benoit, name: Arthur Böök, name: Tarek Dakhran, name: Song Duong, name: Alfred Eng, name: Fernando Fernandes, name: Marc Härkönen, name: Anne Harrington, name: Ramin Hasani, name: Saniya Karwa, name: Yuri Khrustalev, name: Maxime Labonne, name: Mathias Lechner, name: Valentine Lechner, name: Simon Lee, name: Zetian Li, name: Noel Loo, name: Jacob Marks, name: Edoardo Mosca, name: Samuel J. Paech, name: Paul Pak, name: Rom N. Parnichkun, name: Alex Quach, name: Ryan Rogers, name: Daniela Rus, name: Nayan Saxena, name: Bettina Schlager, name: Tim Seyde, name: Jimmy T. H. Smith, name: Aditya Tadimeti, name: Neehal Tumma
Organization: Liquid AI

We present LFM2, a family of Liquid Foundation Models designed for efficient on-device deployment and strong task capabilities. Using hardware-in-the-loop architecture search under edge latency and memory constraints, we obtain a compact hybrid backbone that combines gated short convolutions with a small number of grouped query attention blocks, delivering up to 2x faster prefill and decode on CPUs compared to similarly sized models. The LFM2 family covers 350M-8.3B parameters, including dense models (350M, 700M, 1.2B, 2.6B) and a mixture-of-experts variant (8.3B total, 1.5B active), all with 32K context length. LFM2's training pipeline includes a tempered, decoupled Top-K knowledge distillation objective that avoids support mismatch; curriculum learning with difficulty-ordered data; and a three-stage post-training recipe of supervised fine-tuning, length-normalized preference optimization, and model merging. Pre-trained on 10-12T tokens, LFM2 models achieve strong results across diverse benchmarks; for example, LFM2-2.6B reaches 79.56% on IFEval and 82.41% on GSM8K. We further build multimodal and retrieval variants: LFM2-VL for vision-language tasks, LFM2-Audio for speech, and LFM2-ColBERT for retrieval. LFM2-VL supports tunable accuracy-latency tradeoffs via token-efficient visual processing, while LFM2-Audio separates audio input and output pathways to enable real-time speech-to-speech interaction competitive with models 3x larger. LFM2-ColBERT provides a low-latency encoder for queries and documents, enabling high-performance retrieval across multiple languages. All models are released with open weights and deployment packages for ExecuTorch, llama.cpp, and vLLM, making LFM2 a practical base for edge applications that need fast, memory-efficient inference and strong task capabilities.