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---
title: "Goose and Qwen3 for local execution"
description: "Qwen3 and tool calling with goose, an example local workflow"
title: "Goose and Qwen3 for Local Execution"
description: "Run AI commands locally with Goose and Qwen3 for fast, offline tool execution"
authors:
- mic
---
![local AI agent](goose-qwen-local.png)
A couple of weeks back Qwen 3 (https://qwenlm.github.io/blog/qwen3/) launched with a raft of capabilities and sizes.
This model showed promise and even in very compact form, such as 8B parameters and 4bit quantisation, was able to do tool calling successfully with goose.
Even multi turn tool calling.
A couple of weeks back, [Qwen 3](https://qwenlm.github.io/blog/qwen3/) launched with a raft of capabilities and sizes. This model showed promise and even in very compact form, such as 8B parameters and 4bit quantization, was able to do tool calling successfully with goose. Even multi turn tool calling.
I haven't seen this work at such a scaled down model so far, so this is really impressive and bodes well for both this model, but also future open weight models both large and small.
I would expect the Qwen3 larger models work quite well on various tasks but even this small one I found useful.
I haven't seen this work at such a scaled down model so far, so this is really impressive and bodes well for both this model, but also future open weight models both large and small. I would expect the Qwen3 larger models work quite well on various tasks but even this small one I found useful.
# Local workflows and local agents
<!-- truncate -->
## Local workflows and local agents
For some time I have had a little helper function in my `~/.zshrc` file for command line usage:
@@ -30,7 +30,7 @@ command_not_found_handler() {
This makes use of a zsh feature (zsh now being standard on macos) that will delegate to that function if nothing else on the command line makes sense.
This lets me either make typos or just type in what I want in the command line such as `$> can you kill whatever is listening on port 8000` and goose will do the work, don't even need to open a goose session.
With Qwen3 + ollama running all locally with goose, it worked well enough I switched over to a complete local version of that workflow:
With Qwen3 + Ollama running all locally with goose, it worked well enough I switched over to a complete local version of that workflow which works when I am offline, on the train etc:
```zsh
command_not_found_handler() {
@@ -40,30 +40,33 @@ command_not_found_handler() {
}
```
which works when I am offline, on the train etc.
# Qwen3 reasoning
By default Qwen 3 models will "think" (reason) about the problem, as they are general purpose models, but I found it was quicker (and worked better for my purpose) to make it go into this reason stage.
By adding `/no_think` to the system prompt, it will general skip to the execution (this may make it less successful at larger tasks but this is a small model for just a few turns of tool calls in this case).
## Qwen3 reasoning
I made a small tweak to the default Ollama chat template here: https://ollama.com/michaelneale/qwen3 which you can use as above that you can use as above, if you like (or the default `qwen3` model hosted by ollama also works fine out of the box)
## advanced tips
By default Qwen 3 models will "think" (reason) about the problem, as they are general purpose models, but I found it was quicker (and worked better for my purpose) to make it skip this reasoning stage.
You can use the goose `/plan` mode with a seperate model (perhaps Qwen3 with reasoning, or another model such as deepseek) to help plan actions before shifting to Qwen3 for the execution (via tool calls).
By adding `/no_think` to the system prompt, it will generally skip to the execution (this may make it less successful at larger tasks but this is a small model for just a few turns of tool calls in this case).
It would be interesting to try the larger models if, you have access to hardware (I have only used the 8B parameter one).
My current setup is a 64G M1 pro macbook (circa 2022 hardware) which has probably less than 48G available to use for GPUs/AI, which puts a limit on what I can run, but qwen3 with "no think" mode works acceptably for my purposes.
I made a [small tweak to the default Ollama chat template](https://ollama.com/michaelneale/qwen3) which you can use as above that you can use as above, if you like (or the default `qwen3` model hosted by Ollama also works fine out of the box).
## Advanced tips
You can use the goose `/plan` mode with a separate model (perhaps Qwen3 with reasoning, or another model such as deepseek) to help plan actions before shifting to Qwen3 for the execution via tool calls.
It would be interesting to try the larger models if, you have access to hardware (I have only used the 8B parameter one). My current setup is a 64G M1 pro MacBook (circa 2022 hardware) which has probably less than 48G available to use for GPUs/AI, which puts a limit on what I can run, but qwen3 with "no think" mode works acceptably for my purposes.
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