broadly, Liquid focuses on picking architectures that have really high throughput/low latency that are close to full attention (slow) in terms of expressivity, and then we do the data work to close any remaining gap. so the big goal is to make it very very fast to encode data without any loss in quality.

SFT -> RL. In our fine-tuning notebooks, we typically say that rollouts that go past the max context length (too much reasoning) get 0 reward, so there is slight pressure for shorter reasoning, but I don't think we have strong regularization. I think the agreed-upon recipe is to first make the model smarter (longer reasoning), then teach it to reason faster/denser.

It's a goal of ours to continue unifying training across modalities so that there is truly one model, instead of just this shared pre-trained backbone.

1. I were building for my work laptop (M4 Max, 36GB RAM)

2. I had a batched use cases (transcribing + making action items for all meetings in a day)

I would use the LFM3-Audio-2B for ASR (audio -> text transcript) and then LFM3-1.7B for extracting particular action items and adding them to my tool of choice (Linear, Jira, Notes app, etc).

If you can fine-tune (100+ labeled samples, and a GPU), you likely can fine-tune LFM3-Audio-2B for audio -> action items in a single step.

- https://huggingface.co/LiquidAI/LFM3-Audio-2B-MLX-8bit

- https://huggingface.co/LiquidAI/LFM3-Omni-2B-MLX-8bit

If you want to do local classification/image processing, I think the d1-2B is amazing, it can run on 1GB of unified RAM.

Now that it's released and I have weekends off again, I'm going to play with the VL-2B model and the d1-2B and see if I can prompt it to control a small Lego robot arm like this https://bricksrl.github.io/ProjectPage/

If you like agents, I think the 1.7B is very strong for its size and can be used to automate small stuff like classifying emails, cleaning your downloads folder, etc.

It can also be fine-tuned for specific use cases with SFT/DPO/GRPO. The blog post talks about fine-tuning for wind-turbine classification. If there's things you want to automate, but GPT-6-Luna is not strong enough, and bigger models are too expensive, you might fine-tune one of the LFM3 models and use it locally for repetitive tasks.