From mboxrd@z Thu Jan 1 00:00:00 1970 Received: from outgoing.mit.edu (outgoing-auth-1.mit.edu [18.9.28.11]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by smtp.subspace.kernel.org (Postfix) with ESMTPS id C745F2DB794 for ; Fri, 17 Jul 2026 02:28:01 +0000 (UTC) Authentication-Results: smtp.subspace.kernel.org; arc=none smtp.client-ip=18.9.28.11 ARC-Seal:i=1; a=rsa-sha256; d=subspace.kernel.org; s=arc-20240116; t=1784255283; cv=none; b=sAkGWcoFE5WIoBNBYClpQTQ3isiSbHUtJSHzwur3YUKlL3W/8cKU5RuLT74/GeCZB/XGN/TAcHuoHzL300CGITu+G61+iyPhA7Wirq3z5xb9wejpw3ymLU/526TLNRvs7rylZ+QMXohe7eW4RDsXH00IfyfP696wVj8AycKP0s0= ARC-Message-Signature:i=1; a=rsa-sha256; d=subspace.kernel.org; s=arc-20240116; t=1784255283; c=relaxed/simple; bh=hEdc/c8ymOhlKVwI4ODrQi7p9JF38n+qBaaGLvPv6RM=; h=Date:From:To:Cc:Subject:Message-ID:References:MIME-Version: Content-Type:Content-Disposition:In-Reply-To; b=DMeRdlMuGezeIq3+z4GhjORAJgA5ZnlOEQhELjOlWb1JRypVXv/9AL7qmKHB+DhPpeXYIksQkUBHfZToxTcb3GIKZ63SIYqF3ZYeAuOXteDaPcuUmPge8UAOfLKy6JlBHJWLbbmZXuIElgIbvSjsgUi7O9IJsDsm9uCHl1NfQ6A= ARC-Authentication-Results:i=1; smtp.subspace.kernel.org; dmarc=pass (p=none dis=none) header.from=mit.edu; spf=pass smtp.mailfrom=mit.edu; dkim=pass (2048-bit key) header.d=mit.edu header.i=@mit.edu header.b=jSFQas1q; arc=none smtp.client-ip=18.9.28.11 Authentication-Results: smtp.subspace.kernel.org; dmarc=pass (p=none dis=none) header.from=mit.edu Authentication-Results: smtp.subspace.kernel.org; spf=pass smtp.mailfrom=mit.edu Authentication-Results: smtp.subspace.kernel.org; dkim=pass (2048-bit key) header.d=mit.edu header.i=@mit.edu header.b="jSFQas1q" Received: from macsyma.thunk.org ([151.240.45.27]) (authenticated bits=0) (User authenticated as tytso@ATHENA.MIT.EDU) by outgoing.mit.edu (8.14.7/8.12.4) with ESMTP id 66H2RsEQ000828 (version=TLSv1/SSLv3 cipher=DHE-RSA-AES256-GCM-SHA384 bits=256 verify=NOT); Thu, 16 Jul 2026 22:27:55 -0400 DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=mit.edu; s=outgoing; t=1784255276; bh=yQG5fn8u+h0tJQAvvmHUJexbO7DsNSmLhHLTELTH/Mc=; h=Date:From:Subject:Message-ID:MIME-Version:Content-Type; b=jSFQas1qOyW1hH+sMgpUwBnUs1ilkG/5drTkQ59BgZ9FIpl77Bhd3eCf6hsECPqQV G69y3b0G7RsRL2xL7wE322jd/AIgf7FisD1H4KowWXtgvbSiU+gTWtSK2D7A87EbEW B+RAEt6vAXmL704tbO7vGORLhyWoNRtccx+rV33SFpKsW2Mj3CcDOUQo8Ny9OlKg76 dZmHUfUYBN29NJZx8y+f2RAte/mXp/6CBREP/xsuOnb3j7Dywl5YgqCo9Ag4G6jn8+ bSqrbcfJmPGKuhFDVHXBNBJPmVRcxzSNNyq6QoqBlj5iEs7crvU07N99u4NKIjuxNw X7rfuQBKpKzUw== Received: by macsyma.thunk.org (Postfix, from userid 15806) id 732F8A5ECF4; Thu, 16 Jul 2026 22:27:54 -0400 (EDT) Date: Thu, 16 Jul 2026 22:27:54 -0400 From: "Theodore Tso" To: Mauro Carvalho Chehab Cc: Jonathan Corbet , Sasha Levin , ksummit@lists.linux.dev Subject: Re: [MAINTAINERS SUMMIT] Other LLM-related topics - tags, newcomers, etc Message-ID: References: <87wluv7yzc.fsf@trenco.lwn.net> <87y0fa7pdm.fsf@trenco.lwn.net> <20260716215342.30e44c2f@foz.lan> <20260717025812.2397d792@foz.lan> Precedence: bulk X-Mailing-List: ksummit@lists.linux.dev List-Id: List-Subscribe: List-Unsubscribe: MIME-Version: 1.0 Content-Type: text/plain; charset=us-ascii Content-Disposition: inline In-Reply-To: <20260717025812.2397d792@foz.lan> On Fri, Jul 17, 2026 at 02:58:12AM -0500, Mauro Carvalho Chehab wrote: > > 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. Fair; but I do think we need to fine-tune it with a lot LKML text so that it has the knowledge that we actually need for kernel development before I'd trust the smaller models. > 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 (*) I've been playing with qwen3-next with 256k context context size, 80B parameters (3B activated), with 8bit quantization, but I haven't been willing to trust it with generating kernel code. I have experimenting to see how it compares with Gemini 3.1 when creating a python script to send e-mail[1] or creating a bash completion script[2] for my fstests test appliance. [1] https://github.com/tytso/xfstests-bld/commit/dfadb2014da446ecb967de51904ee531f7be8bd5 [2] https://github.com/tytso/xfstests-bld/commit/dfadb2014da446ecb967de51904ee531f7be8bd5 However, from Roman tells me, Sashiko is running muliple LLM passes using a frontier model for each commit review. So what Sashiko does is quite a bit more complicated than a series of prompts such as: Create a python program which submits an e-mail message using the Submission Port (port 587), It should enable encryption using STARTTLS and it should obtain the username and password from a config.ini file. Model the python program using the send-mail.py in the sandbox directory. It should support the same command-line options but instead of sending the e-mail using sendgrid, it should send the e-mail using the Submission protocol. Please add pydoc documentation to send-mail-smtp.py. Please enhance the program you just created (send-mail-smtp.py) to support specifying a path to a certificate file in the configuration file in case the user doesn't want to use the system provided top-level trusted certificates. Please add support for a configuration file parameter which specifies whether TLS should be mandatory, optional, or disabled. Update the pydoc documentation as necessary. Please integrate the functionality found in send-mail.py into send-mail-smtp.py. Instead of getting the Sendgrid API key from an environment variable, change it to obtain the Sendgrid API key from the configuration file. If the Sendgrid API key is specified, use sendgrid instead of the SMTP submission protocol. Update the pydoc documentation strings. .... which does work pretty well even on less capable models that I can run locally. I guess we could try running Sashiko using ollama-mlx on a Macbook with 128GB, and see how it works, but my assumption is that the answer is "not well" --- which is why I really want to look at fine-tuning one of these smaller models first. Cheers, - Ted