Liquid AI Ships LFM2.5-230M with llama.cpp, MLX, vLLM, SGLang, and ONNX Support for On-Device Inference

Liquid AI shipped LFM2.5-230M , it’s the company’s smallest model to date. The release targets a specific job: running agentic tasks on phones, robots, and automation devices. Both the base and instruction-tuned checkpoints are open-weight on Hugging Face.

The pitch is narrow on purpose. This is not a general reasoning model. It is built for data extraction and tool use on edge hardware.

LFM2.5-230M is a 230-million-parameter, text-only model. It is built on the LFM2 architecture. The model has 14 layers total. Eight are double-gated LIV convolution blocks. The remaining six are grouped-query attention (GQA) blocks. The hybrid layout targets fast CPU inference.

The context length is 32,768 tokens. The vocabulary size is 65,536. The knowledge cutoff is mid-2024. It supports ten languages, including English, Chinese, Arabic, and Japanese.

Liquid AI team ships two checkpoints. LFM2.5-230M-Base is the pre-trained model for fine-tuning. LFM2.5-230M is the general-purpose instruction-tuned version. The license is lfm1.0.

The model was pre-trained on 19 trillion tokens. That total includes a 32K context extension phase. The post-training recipe then runs in three stages.

First comes supervised fine-tuning with distillation from the larger LFM2.5-350M. Second is direct preference optimization (DPO). Third is multi-domain reinforcement learning. This preserves flexibility for downstream specialization.

The distillation step is what keeps a 230M model competitive with larger checkpoints. It inherits behavior from the bigger LFM2.5-350M on targeted tasks.

Liquid AI team evaluated LFM2.5-230M across ten benchmarks. They span knowledge, instruction following, data extraction, and tool use.

The instruction-following results support that. On IFEval, LFM2.5-230M scores 71.71. That beats Qwen3.5-0.8B (59.94) and Gemma 3 1B IT (63.49). On IFBench it scores 38.40, ahead of both. On CaseReportBench, a clinical data-extraction test, it scores 22.51.

LFM2.5-230M leads on instruction following and data extraction. It trails on broad knowledge: MMLU-Pro is 20.25, behind Qwen3.5-0.8B’s 37.42. It is also weak on some agentic tool use. On τ²-Bench Telecom it scores just 5.26.

Liquid AI is direct about the limits. It does not recommend the model for reasoning-heavy workloads. That means advanced math, code generation, and creative writing.