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Liquid AI releases LFM3: six open-weight ~2B models for text, vision, audio, omni and decisions

Big news

Liquid AI's LFM3 family has six roughly 2B-parameter open-weight models sharing one backbone trained on 22T tokens, which mixes short convolutions with sliding-window and global attention. Variants cover 256K-context text, image and video input, audio, omni input, and two decision models. Weights use Liquid's LFM Open License v1.0.

Why it matters

A capable on-device family where the 4-bit text model fits in about 1GB, with day-one support in llama.cpp, MLX, vLLM, Ollama and LM Studio.

How to try it

LFM3-1.7B is the text model of the family, small enough to run on a laptop. Liquid publishes GGUF builds for llama.cpp and recommends low-temperature sampling for chat, tool use and coding.

  1. Install llama.cpp (or use Ollama, LM Studio or MLX, which Liquid lists as supported).
  2. Run the official GGUF build straight from Hugging Face with the command below.
  3. For Python, load LiquidAI/LFM3-1.7B in a Transformers text-generation pipeline with trust_remote_code=True.
bash
llama-cli -hf LiquidAI/LFM3-1.7B-GGUF \
--temp 0.1 --top-k 50 --repeat-penalty 1.1

Open the official docs

Source: Read the full story on Liquid AI

small-modelson-deviceliquid-aimultimodalopen-weights

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How to try it

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