Liquid AI releases LFM3: six 2B-class multimodal models on one tri-hybrid backbone, open weights on Hugging Face

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Liquid AI releases LFM3: six 2B-class multimodal models on one tri-hybrid backbone, open weights on Hugging Face

Liquid AI released LFM3, six 2B-class open-weight models (text, vision, audio, omni, decision) all branched from the same LFM3-1.7B-Base backbone, available on Hugging Face, the Playground and the Liquid AI API.
LFM3-1.7B beats the earlier LFM2.5-2.6B with 35% fewer parameters, using a 24-layer tri-hybrid mix of 13 gated short convolutions, 5 sliding-window attention layers with a 256-token window, and 6 global attention layers.
Pre-training covered 22T tokens, and the last 3.5T run as a curated mid-training stage heavy in agentic traces and tool calls that also extends the text model context to 256K tokens.
The text model fits in about 1 GB at 4-bit precision, runs roughly 25% faster than LFM2-1.2B once 8K tokens are in context, and every model in the family can be fine-tuned on a single GPU.

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I worked on this release, so happy to take questions on local models vs cloud frontier models.

Okay but why should I care? Skimming the doc all I get is 'specialized use cases', and I have the attention span of a gnat, so what does that actually buy me?

Funny thing in the reasoning trace: 'However, the instructions say I must use available tools when answering questions.' I asked what animal lays the biggest egg with no system prompt and it still went there, so that's baked in from all the RL on tool use.

Curious whether this is actually useful for OCR and parsing PDFs and DOCX files. Has anyone tried it?

The live mode restarts the text every 30 seconds, which seems rough when the thing you want gets agreed over several minutes. For action items out of an hour-long recording, do I go straight from audio with LFM3-Audio-2B or transcribe first and feed the text to LFM3-1.7B? And how many labeled examples would fine-tuning that take?

Is Omni worse than VL at vision, or roughly the same? Do I pick the model that matches the capability set I want, or just use Omni for everything?

Did you do specific work that makes these good for moderation? On local models people push their hardware to the limit and there's no headroom left to constrain output for kids or schools. Some older LFM versions were way better at this than much larger models.

I use LFM 2.6 to annotate emails and messages into one big database on an RTX A4500. Low-confidence annotations trickle up to DeepSeek 4.1 Flash for a redo. It gets trustworthy annotations on just over 40% of data points now, and I'm waiting for LFM3 in LocalAI.

Why not fine-tune it on your own dataset instead of waiting for the upgrade?

I'd point you at gliner2.5 for tagging. Fine-tuned on my 5090 in a few minutes on 500 articles labeled by gpt 6.1, and it runs on a Hetzner CPU.

I'm prototyping the same metadata enrichment and drowning in options. Small qwen plus RAG, or fine-tuning, or layering the decision models like a binary search over a pre-generated corpus?

Do you need a GPU for email triage at all? There are CPU-only open source classifiers you can self-host and train on your own data.

Worth trying the decision models too, d1-2B is strong for calibrated classification.

How are you getting those confidence levels, logprobs?

Agentic coding numbers land on par with Qwen3.5-4B and the rest on par with Qwen3.5-9B. That is impressive for a 1.7B model.

Is anyone seriously going to use a model this small for coding? Doesn't feel like the intended use case or the strength here.

I've run 2.6B as my main home assistant model and switched to Ling 3.0 Tiny because A1B is faster. Really hoping 1.7B is good enough, since 2.5 was too dumb for HA.

Tried both the Omni and the text-only model as MLX builds in LM Studio and neither would load, kept throwing safetensors read errors.

mlx-vlm should support the Audio and Omni models, and there are native MLX 8-bit exports on Hugging Face for both.

LFM3-VL-2B gave me better text answers than LFM3-1.7B in the playground. 1.7B said a cow says 'Oink'. The Q8 versions through llama both say 'moo', so I'd blame quantization.