Comment by h14h
3 hours ago
That's not all that surprising, IMO. From what I understand, LiquidAI is focusing pretty narrowly on building models that operate as the "agentic core" of a larger system.
If I were going to use this model, I'd be looking to use it more as is the primary chat interface of a larger system, and having it orchestrate & delegate tasks to other places via tool calls. It's not quite as exciting on the surface as a local "do it all" model, but it does enable some pretty neat use-cases, IMO.
I'm imagining a local agent that is super low latency, works entirely offline, and capable of queuing up complex tasks for larger/smarter cloud agents which execute them asynchronously.
Interesting...
Two of the other responses speak about it being abysmal at tool calling.
Overall, I'm pretty impressed a model this small can find/fix ~12% of bugs with crappy context - even if they're about as easy as possible to fix.
I just assumed it would perform better, given all the advancements in the space.
It's possible 1B active parameters is just not enough - even if it has 8B params of knowledge to reason through bugs.
Playing around with the context I fed it, it was able to fix up to ~34% of bugs vs ~46% for Qwen2.5-Coder-3B and ~54% for Qwen2.5-Coder-7B.