Re: [MAINTAINERS SUMMIT] Other LLM-related topics - tags, newcomers, etc

[Mauro Carvalho Chehab <mchehab+huawei@kernel.org>]

On Sat, 18 Jul 2026 11:26:27 +0200
Takashi Iwai <tiwai@suse.de> wrote:

> On Fri, 17 Jul 2026 02:58:12 +0200,
> Mauro Carvalho Chehab wrote:
> >   
> > > So it's one say to say, we should figure out how to try to run Sashiko
> > > on a local LLM, using open-weight models.  But it's going to be a lot
> > > easier to propose such a thing than to actually do it.  
> > 
> > The main point is: do we really need 671B parameters? Those models
> > speak a lot of different languages, have medical databases, and a lot
> > of other random knowledge that are useless for kernel development.
> > 
> > I've been playing for a while with qwen 3.6 with 24KB context size,
> > 36B parameters (3B activated), 4bits kv quantization and it does produce
> > some decent results. The main limitation is the context size: it is
> > probably not big enough to test big files (*)
> > 
> > (*) my GPU has 16GB and it is not dedicated to LLM - still, it does
> >     present results on a reasonable time (a couple of minutes) and
> >     with decent precision.  
> 
> I've been testing the kernel commit reviews with 16GB VRAM GPU, too.
> It's an agent program based on Chris Mason's review prompts, and
> targeted mainly for the verifications of our backport patches.  The
> main models I tried were Qwen-3.6 35B, Gemma-4 26B and GPT-OSS 20B
> (all 4-bit quantized).  And, my conclusion was that even such small
> models can catch real bugs.
> 
> Yes, there are definitely many false-positives, and they don't always
> follow the recent changes.  Also they cover much less changes than
> Sashiko.  So, the results must be read with lots of grains of salt :)
> 
> The code review is different from the code generation, and the agent
> divides the tasks so that the model can concentrate on each small
> single task.  With that, small models can achieve in some level, too.
> 
> 
> FWIW, the agent code is found at
>   https://github.com/tiwai/kernel-review-agent.git
> 
> with HTML renderer and console viewer programs
>   https://github.com/tiwai/review-table-gen.git
>   https://github.com/tiwai/kreview-ui-rs.git

I just did a Sashiko review test using Ollama locally after applying 
this PR:

	https://github.com/sashiko-dev/sashiko/pull/338

The code was AI generated with qwen3.6 with Claude Opus reasoning
(hf.co/rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF:Q4_K_M).

I'll try to play with it a little bit to see how it works with
real patch reviews.

Thanks,
Mauro