Have you tried the Gemma 4 series, out of curiosity? I haven’t run a local model in a while, but the benchmarks look good. I’d take a free local tool-use model if it was relatively consistent.
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.
My config is similar to: https://github.com/noonghunna/club-3090/blob/master/docs/eng...
I need to try out some of the other set ups mentioned in this repo for increased TPS.
Both 27b and A3B done all my production works pbeautifuly (At Q8) i dont think any model are good for Q4.
Qwen 3.5 122b surpasses both of them tho.
The difference basically boils down to Gemma 4 making more assumptions and Qwen 3.6 sticking closer to the prompt, if your prompt is bad or leaves things up to the imagination, Gemma will do a better job, if you need strict prompt adherence Qwen is better. Since local models are "dumb" i think it makes sense to prefer prompt adherence, but there are complex tasks that Gemma will complete much much faster than Qwen because it makes the right assumptions the first time and as a result even with slower inference requires way fewer turns.
My speculation is that this comes from google having a much better strategy for filtering their training data, I think this also shows up in the shape of the world knowledge of the models. Gemma's world knowledge seems deeper even though the models are of roughly equivalent size to the Qwen counterparts so it's mostly likely just concentrated in places that are more relevant to my queries.
Most notably in my testing, Gemma 4 31b is the ONLY local model that will tell me the significance of 1738 correctly. Even most flagship/cloud models answer with some hallucinatory nonsense.
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.
So far the only tools the agent has access to are `evaluate_commands(commands=["...", "..."])` and `get_buffer_contents()`, which really makes them have to work for doing things. I could make it super easy for them but then it wouldn't be an interesting experiment.
If I were to try to make something more useful out of this, I'd probably add the ability for LLMs to list buffers, probably give them an easier out for executing shell scripts in the way they prefer, give them an easier time to list docs and a few other things like that.
The tools and the interaction with Kakoune is really trivial to write; I already use this by having the agent write to the session FIFO (a very simple binary format) and I extract information via my own FIFO that Kakoune writes to (this is used for the buffer data only right now).
I think once you started using it more as a tool and not a pseudo-benchmark like I am you'd probably think of even more things to add but a lot of it comes down to just making Kakoune's state visible and making shell spam (which the LLMs love) easier.
The Qwen models are quite solid though.
Can you share your switches and approach for using tools?
My setup is a bit of a mess as I experiment with different ways of configuring and hosting local models. So at some point I was experimenting with the router server but stopped doing that, but some of my settings are still in models.ini while some are on the command line.
podman run --env "HF_TOKEN=$HF_TOKEN" --env "LLAMA_SERVER_SLOTS_DEBUG=1" -p 8080:8080 --device /dev/kfd --device /dev/dri --security-opt seccomp=unconfined --security-opt label=disable --rm -it -v ~/.cache/huggingface/:/root/.cache/huggingface/ -v ./unsloth:/app/unsloth -v ./models.ini:/app/models.ini llama.cpp-rocm7.2 -hf unsloth/gemma-4-31B-it-GGUF:UD-Q8_K_XL --chat-template-file /root/.cache/huggingface/gemma-4-31B-it-chat_template.jinja -ctxcp 8 --port 8080 --host 0.0.0.0 -dio --models-preset models.ini
With the following as the relevant settings in models.ini (I actually have no idea if these settings are applied when not using the router server, it's been hard for me to figure out what settings are actually applied when using bot the command line and models.ini
[*]
jinja = true
seed = 3407
flash-attn = on
[unsloth/gemma-4-31B-it-GGUF:UD-Q8_K_XL]
temperature = 1.0
top_p = 0.95
top_k = 64
As my harness, I'm using pi, with a pretty vanilla config.
Anyhow, Gemms 4 31b worked in this config, but it was slow and RAM hungry. Since then, I've mostly moved to Qwen 3.6 35b-a3b because it's a lot faster.
I'm not actually doing anything useful with these yet, but I've used them for some experiments and Qwen 3.6 35b-a3b was capable of doing some pretty long mostly unsupervised agentic loops in my experimentation.
Maybe it could be fun to hook them up via a2a protocol as left and right brain agents operating in tandem.
>Maybe it could be fun to hook them up via a2a protocol as left and right brain agents operating in tandem
What in the world does this even mean?
Not for creative writing or NLP.
I have not benchmarked Qwen3.5 vs. Qwen3.6 for the same task, nor trialed Gemma4-26B. Guess it's time for some testing!
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.
Also, they're good enough for a lot of simple categorization and data extraction tasks, e.g. something like "flag abusive posts/comments", or "visit website, find the contact info, open hours, address". And they run fast on the kind of hardware you're likely to have at home, while the bigger dense versions decidedly do not.
I used Gemma 4 itself to review and prune the data (my social media posts over the last ~5 years, about 5 million words) being ingested into the training process for a LoRA for Gemma 4. I found the bigger model (31B) was more nuanced and useful than the smaller ones, and I wasn't in a big hurry by that stage of the process, so I used the big one overnight. Gemma 4 31B was also a better judge of my writing than Gemini Flash 2.5, by my reckoning.
It was, again, more nuanced, and was able to recognize a generally helpful comment that opened kinda jokey/rude, while the smaller model and Gemini 2.5 Flash tended to gravitate toward extremes (1 or 5) rather than the 1-5 scale they were prompted to rate on. I assume Gemini 3.1 Flash is probably competitive or better, but I didn't try it, since I liked the results the self-hosted Gemma 4 was giving for free.
The little ones also run great on very modest hardware. Both run at comfortable interactive speed mid-range tablets. E4B is blazing fast on an iPad M4 or Pixel 10 Pro and entirely usable on a midrange Android with sufficient RAM.