This is super interesting, I'm particularly excited for this one as it may allow teams to scale this architecture for VLAs (vision language action models), and having sparser models means more real-time actions on a locally hosted model
I'm surprised these models haven't picked this up yet in the training data. Both Claude and ChatGPT missed that one when I posed the question to them last year.
Why does this not have (day-one) support for Ollama? The previous model is on there? Is it related to the ongoing refactor work or are people abandoning Ollama for other LLM engines?
Wow, this is fucking phenomenal. I fed it a long transcript asking it to create a summary and it executed it extremely well. For an 8B model this is quite impressive.
Anybody use their localcowork [1] before? That is where the demo lives. Or not?
[1] https://github.com/Liquid4All/cookbook/tree/main/examples/lo...
The small models are getting really impressive.
I recently realized that Qwen3.5:4B is way more capable than I thought a model that size could be.
Combine that with the work Liquid puts into RL and fine tuning, and you get models that perform extremely well on minimal hardware.
Combine that with your own fine tuning, and you get a specialized tool that is fast, private, and doesn’t require internet connection.
What did you use qwen3.5 4b for?
This is super interesting, I'm particularly excited for this one as it may allow teams to scale this architecture for VLAs (vision language action models), and having sparser models means more real-time actions on a locally hosted model
demo link for anyone that wants to try this out https://playground.liquid.ai/chat?model=cmppnbgse000004l4bc8...
They seem… much better than all the models they compared against? What’s the catch?
Question: I have a dirty car and the car wash is just 50 meters away. Should I walk or drive to the carwash?
Answer: . . . . So, unless you have a compelling reason not to, walk to the car wash.
I'm surprised these models haven't picked this up yet in the training data. Both Claude and ChatGPT missed that one when I posed the question to them last year.
Guess we can run this even on CPU!
Why does this not have (day-one) support for Ollama? The previous model is on there? Is it related to the ongoing refactor work or are people abandoning Ollama for other LLM engines?
Ollama is just llama.cpp but with their own interface ontop. Liquid does support llama.cpp, but Ollama is slow in updating its llama.cpp dependency.
It does, ollama pull maternion/lfm2.5
Wow, this is fucking phenomenal. I fed it a long transcript asking it to create a summary and it executed it extremely well. For an 8B model this is quite impressive.
Homeopathic AI