samstevens

speed :)

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.

my intuition is that cross modality improvements do not really show up in practice, and especially not in small models

unfortunately it's not a strong multi-speaker/diarized output audio model. It's a future goal but it doesn't have a timeline. If you have the hardware, I think fine-tuning might be possible.

interesting, I didn't see it on the playground or on my local llama-server.

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.

the playground is bf16 or fp8 depending on the cloud GPU for the playground. I didn't have any issues with cows saying moo but I suspect it's just LLM non-determinism :)

definitely would not recommend using it for SWE-style tasks. But chaining a couple bash calls together to find a file? no problem

Can you describe more in terms of what moderation you want? I can say with confidence that we don't train for the typical adult-only content you can see on some "de-safe'd" models, but it's not clear to me exactly what you're asking about. We can also discuss over email if you prefer.

unfortunately the models are still stronger without extra modalities. So LFM3-1.7B is better at pure text tasks than all other models. the LFM3-VL-2B is better at text/vision tasks than the Omni, the Audio model is a stronger ASR/TTS model than the Omni, etc.

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.

It all depends on your hardware and throughput requirements. Suppose

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.

You might also consider the decision models, d1-2B is very strong and good for calibrated classification. Would be happy to share links/resources if you're interested.

quantization is always a problem; what quant were you using?

what does your home assistant do? a general siri/alexa-style model? or something else?