Liquid LFM2.5-8B First Test – An On-Device MoE Model!

start game now and it hyperl it just to the actual Python script. I like that. Today we're going to be taking a look at a new model from Liquid AI called LFM2.5-8b- A1B. Now, as the name may have tipped you off to, this is an 8 billion parameter mixture of experts model with 1 billion active. And the liquid models are always very interesting because they are essentially designed for ondevice intelligence. So, even one of the most prominently displayed things right here in the key features is an ondevice personal assistant. So, Liquid basically has a lot of small but very intelligent models. I've tested a bunch of their offerings on the channel in previous videos and they've always been very impressive, especially when considering the size. So, I'm excited to test this, which is almost kind of like a larger model, at least in terms of their offerings. So before we get into it, please do feel free to subscribe and let's just begin by taking a look at this model and some of the interesting things of note about it. So as we saw in the beginning, one of the prominently listed things here is that this is designed to power real life applications, chaining tool calls and following complex instruction on devices. So this model seems specifically poised towards, as it says right there, almost a gentic functionality where it performs tool calls and is good at doing that. Apparently, as they list right here, and from what we can see in this benchmark JPEG, this does seem to stack up very favorably against some models that are quite a bit larger, at least in terms of total parameter count, not as much in terms of active parameter count, but still this does seem like a very, very interesting benchmark JPEG because every other model that is listed here is bigger and some of them even like a decent amount. So like we can see here the Gemma 426B instruction tune model has four times as many active parameters 1 billion versus 4 billion and the Quen has three times as many and these are all very performant models. Even GPT OSS20B is still a very very performant model for specific use cases. So this benchmark chart right here does make one rather excited to test the raw capabilities of this model. Now, if we look in the dedicated blog post for this model's announcement, there's kind of the same of what we saw in the hugging face model card where we have this benchmark chart. However, something I find very interesting is that they do mention this is a buildup of the previously released LFM2 8B A1B model.