Releases from frontier labs, major companies and research institutes — grouped by model, deduplicated across HuggingFace, OpenRouter and official blogs.
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Liquid AI🇺🇸Major lab
5-bit MLX build of LFM3-1.7B for Apple Silicon, run through mlx-vlm 0.7.11 or later. Optional thinking mode via a flag, with 12 listed languages. Loading in mlx-lm is still pending upstream.
Liquid AI🇺🇸Major lab
LFM3-Omni-2B is Liquid AI's first omni-modal model in the LFM3 family: a single end-to-end model that understands text, images, video and audio, and answers in text and speech. It is built for on-device use: one model replaces a separate ASR model, vision-language model, LLM and TTS model.
Liquid AI🇺🇸Major lab
LFM3-VL-2B is a vision-language model for on-device visual understanding. It takes text, images, and video as input, and combines the LFM3 language model with a new in-house vision encoder.
Liquid AI🇺🇸Major lab
d1-omni-600M is a 600M parameter decision model built on LFM2.5-Encoder-350M. You give it a state (text or JSON, with images or a voice clip) and a set of named questions.
Liquid AI🇺🇸Major lab
d1-3B is a 3B parameter decision model built on LFM2.5-VL-3B. You give it a state (text, JSON, images, or a mix) and a set of questions.
Liquid AI🇺🇸Major lab
LFM2.5-VL-3B is a multimodal variant of LFM2.5, a family of hybrid models designed for on-device deployment. It builds on LFM2-VL-3B with further mid- and post-training.
Liquid AI🇺🇸Major lab
LFM2.5-Encoder is a family of multilingual bidirectional encoders built on the LFM2 architecture, available in two sizes:
Liquid AI🇺🇸Major lab
LFM2.5 is a hybrid model designed for on-device deployment, featuring 230 million parameters and optimized for fast edge inference. It is suitable for general-purpose text generation and agentic tasks, but not recommended for reasoning-heavy workloads.