Now, is this the usual use case? No, it's a benchmark I created specifically in order to put LLMs in situations where they can't just blast out their bash commands without having to interface with something else and adapt.
Can you share some parameters you enable tool calling and agentic usage?
Or, higher level, some philosophies on what approaches you are using for tuning to get better tool calling and/or agentic usage?
I'm having surprisingly good success with unsloth/Qwen3.6-27B-GGUF:Q4_K_M (love unsloth guys) on my RTX3090/24GB using opencode as the orchestrator.
It concocts some misleading paths, but the code often compiles, and I consider that a victory.
You have to watch it like you would watch a 14 year old boy who says he is doing his homework but you hear the sound effects of explosions.
The Qwen models are quite solid though.
The 4b was okay. It didn't get all of my small math questions right, it didn't know about some of the libraries I use, but it was able to do some basic auto complete type stuff. For microscopic models I like the llama 3.2 3b more right now for what I do, it's a little faster and seems a little stronger for what I do. But everyone is different and I don't think I'll use it anymore this past month has been crazy for local model releases.
curious how people are leveraging these models
Instead of hitting stack overflow and Google I will ask questions like "can you give me an example of how to do x in library y?" Or "this error is appearing what might be happening if I checked a b and c". Or "please write unit tests for this function". Or code auto complete.
I am not looking for the world's best answer from a 3b model. I am looking for a super fast answer that reminds me of things I already know or maybe just maybe gives me a fast idea to stub something while I focus on something more important, I am going to refactor anyways. Think a low quality rubber duck
I mostly use 7-9b models for this now but llama 3.2 3b is pretty decent for not hogging resources while say I have other compute heavy operations happening on a weak computer.
Probably half the questions people ask chatgpt could get roughly the same quality of answer with a small model in my opinion. You can't fully trust an LLM anyways so the difference between 60% and 70% accuracy isn't as much are marketing makes it sound like. That said the quality of a good 7-9b model is worth it compared to a 3b if your machine can run it. Furthermore the quality of qwen 36 is crazy and makes me wonder if I will ever need an AI provider again if the trend continues.
Qwen3.6 raises the bar for models of its size. There really isn't a comparison in my opinion.
Qwen is really good.
Also, generally, it makes sense. 8B models are generally not very good^.
That this 8B model is decent is impressive, but that it could perform on par with a good model 4 times as large is a daydream.
^ - To be polite. The small models + tool use for coding agents are almost universally ass. Proof: my personal experience. Ive tried many of them.
edit: It was a play on The Big Lebowski, folks.
Nor do class standings, nor hackerrank and the like.
What will tell you is asking them to fix a thing in your codebase. Once you ask an LLM to do that, a dozen times, I'd argue it's no longer "just your opinion man", it's a context-engineered performance x applicability assessment.
And it is very predictive.
But it's also why someone doing well at job A isn't necessarily going to be great at B, or bad at A doesn't mean will necessarily be bad at B.
I've often felt we should normalize a sort of mutual try-buy period where job-change seeker and company can spend a series of days without harming one's existing employment, to derisk the mutual learning. ESPECIALLY to derisk the career change for the applicant who only gets one timeline to manage, opposed to company that considers the applicant fungible.
But back to the LLM, yeah, the only valid opinion on whether it works for you is not benchmark, it's an informed opinion from 'using it in anger'.