Is interesting how the live mode is restarting the text every 30 sec, wonder how it can impact a thing that may be agreed over several minutes. Say I would like to get action items out of an hour long recording, am I supposed to go straight from the audio with LFM3-Audio-2B? Or is it transcribe first and give the text to LFM3-1.7B? What if I want to fine-tune the action items routine. How many labelled examples do you estimate such task may need?

I am seeing the following in the reasoning trace:

> However, the instructions say I must use available tools when answering questions.

On "What animal lays the biggest egg?" with no system prompt

I assume this is something baked into the model through lots of RL with tool use?

Was there any intent behind the audio encoder representation or was it frozen backbone -> co adaptation?

In a few words: Why should I care about this?

What makes it cool? What should make me go "oh yeah let's go"?

From what I can see by skimming the document, I would assume "something something specialized use-cases", but what does that mean? And what does it mean assuming I have attention deficit?

Is LFM3-Omni-2B worse or roughly equal at vision tasks compared to LFM3-VL-2B?

In other words: Should I select the model that matches my desired capability set or just use Omni?

Congrats for the achievement!

I'm curious if this model would be useful for OCR or at least text documents parsing (PDF, DOCX, etc.), what do you think?

Did y'all do any particular work that makes these models great for moderation? One of the issues between cloud models vs local is that often people are trying to push the limit of what their hardware can do and it doesn't leave too much headroom for moderation to make the models a little more constrained for kids or schools to use locally, or just for family in general who don't want accidental unhinged results.

In the past I've noticed that some LFM were way better at moderation than much larger models. Not necessarily prejudging what is acceptable, but allowing you to interrogate content with a series of questions to customize the moderation. Other models I tested were far worse.

Decision models are like the next optimization in that direction, but none of the benchmarks for the popular models ever show how good they are at tasks like content moderation. The smaller and more efficient we can get that workload, the more that it can simply be on by default in a lot of use cases without feeling like you're giving up a lot of performance, creative freedom or accuracy. You can't have a high number of false positives or people would rather take control back.

We need local content moderation to be cool, efficient, accurate and configurable so that people can get the higher quality results which unsafe models tend to produce, while filtering out the unsafe results fast enough that it can even be done in an agentic way and fed back into the model to steer its results.