LiquidAI/LFM2-700M

LFM2-700M

LFM2-700M is a 742M-parameter hybrid language model from Liquid AI, purpose-built for edge AI and on-device deployment. Part of the second-generation LFM2 family, it combines a novel architecture of multiplicative gates and short convolutions — specifically 10 double-gated short-range LIV convolution blocks and 6 grouped query attention (GQA) blocks — to deliver strong quality at minimal footprint.

Key Strengths

• Optimized for the edge — Runs efficiently on CPU, GPU, and NPU hardware, making it suitable for smartphones, laptops, and vehicles. LFM2 achieves roughly 2× faster decode and prefill on CPU compared to Qwen3.
• Competitive quality — Outperforms similarly-sized models across knowledge, math, instruction following, and multilingual benchmarks.
• Multilingual — Supports English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish.
• Tool use built in — Natively supports structured function calling via special tokens and JSON schema definitions.

The model supports a 32,768-token context length, uses bfloat16 precision, and was trained on 10 trillion tokens (~75% English, 20% multilingual, 5% code). Training leveraged knowledge distillation from LFM1-7B, large-scale supervised fine-tuning, custom DPO, and iterative model merging.

Performance

LFM2-700M delivers strong results relative to its size class, scoring 49.9 on MMLU, 72.23 on IFEval, and 46.4 on GSM8K — competitive with or exceeding models like Llama-3.2-1B-Instruct and Qwen3-0.6B.

CPU inference throughput is a particular highlight, with LFM2 models outpacing alternatives in both ExecuTorch and llama.cpp runtimes.

Recommended Use

Liquid AI recommends fine-tuning LFM2-700M on narrow use cases for best results. It is well suited for agentic tasks, data extraction, RAG, creative writing, and multi-turn conversations. GGUF quantized checkpoints are also available for llama.cpp deployment. Compatible with Hugging Face Transformers v4.55+ and vLLM v0.10.2+.