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Wed, 22 Jul 2026 09:53:01 +0000 (UTC) Received: from dovecot-director2.suse.de ([2a07:de40:b281:106:10:150:64:167]) by imap1.dmz-prg2.suse.org with ESMTPSA id Suh2L/2SYGoVVgAAD6G6ig (envelope-from ); Wed, 22 Jul 2026 09:53:01 +0000 Date: Wed, 22 Jul 2026 11:53:01 +0200 Message-ID: <87h5lrgxky.wl-tiwai@suse.de> From: Takashi Iwai To: Mauro Carvalho Chehab Cc: Takashi Iwai , "Theodore Tso" , Jonathan Corbet , Sasha Levin , ksummit@lists.linux.dev Subject: Re: [MAINTAINERS SUMMIT] Other LLM-related topics - tags, newcomers, etc In-Reply-To: <20260719112924.2f97086d@foz.lan> References: <87wluv7yzc.fsf@trenco.lwn.net> <87y0fa7pdm.fsf@trenco.lwn.net> <20260716215342.30e44c2f@foz.lan> <20260717025812.2397d792@foz.lan> <87o6g4sl6k.wl-tiwai@suse.de> <20260719112924.2f97086d@foz.lan> User-Agent: Wanderlust/2.15.9 (Almost Unreal) Emacs/30.2 Mule/6.0 Precedence: bulk X-Mailing-List: ksummit@lists.linux.dev List-Id: List-Subscribe: List-Unsubscribe: MIME-Version: 1.0 (generated by SEMI-EPG 1.14.7 - "Harue") Content-Type: text/plain; charset=US-ASCII X-Spam-Flag: NO X-Spam-Score: -1.80 X-Spamd-Result: default: False [-1.80 / 50.00]; BAYES_HAM(-3.00)[100.00%]; SUSPICIOUS_RECIPS(1.50)[]; MID_CONTAINS_FROM(1.00)[]; NEURAL_HAM_LONG(-1.00)[-1.000]; NEURAL_HAM_SHORT(-0.20)[-1.000]; MIME_GOOD(-0.10)[text/plain]; TO_DN_SOME(0.00)[]; FUZZY_RATELIMITED(0.00)[rspamd.com]; MIME_TRACE(0.00)[0:+]; RCVD_VIA_SMTP_AUTH(0.00)[]; ARC_NA(0.00)[]; TAGGED_RCPT(0.00)[huawei]; RCVD_TLS_ALL(0.00)[]; DKIM_SIGNED(0.00)[suse.de:s=susede2_rsa,suse.de:s=susede2_ed25519]; FROM_HAS_DN(0.00)[]; RCPT_COUNT_FIVE(0.00)[6]; FROM_EQ_ENVFROM(0.00)[]; RCVD_COUNT_TWO(0.00)[2]; TO_MATCH_ENVRCPT_ALL(0.00)[]; DBL_BLOCKED_OPENRESOLVER(0.00)[suse.de:email,suse.de:mid,imap1.dmz-prg2.suse.org:helo] X-Spam-Level: On Sun, 19 Jul 2026 11:29:24 +0200, Mauro Carvalho Chehab wrote: > > On Sat, 18 Jul 2026 11:26:27 +0200 > Takashi Iwai 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! Let me know if you can compare the review results from different models qualitatively. It'd be interesting. Takashi