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Guidelines for pre-trained ML model weight binaries (Was re: Where shoul
Guidelines for pre-trained ML model weight binaries (Was re: Where should we put machine learning model parameters?)
Mon, 03 Apr 2023 18:07:19 +0000
Hi there FSF Licensing! (CC: Guix devel, Nicholas Graves) This morning I read
through the FSDG to see if it gives any guidance on when machine learning model
weights are appropriate for inclusion in a free system. It does not seem to
Many ML models are advertising themselves as "open source", including the llama
model that Nicholas (quoted below) is interested in including into Guix.
However, according to what I can find in Meta's announcement
(https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) and the
(https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) the model
itself is not covered by the GPLv3 but rather "a noncommercial license focused
on research use cases." I cannot find the full text of this license anywhere in
20 minutes of searching, perhaps others have better ideas how to find it or
perhaps the Meta team would provide a copy if we ask.
Free systems will see incentive to include trained models in their
distributions to support use cases like automatic live transcription of audio,
recognition of objects in photos and video, and natural language-driven help
and documentation features. I hope we can update the FSDG to help ensure that
any such inclusion fully meets the requirements of freedom for all our users.
------- Original Message -------
On Monday, April 3rd, 2023 at 4:48 PM, Nicolas Graves via "Development of GNU
Guix and the GNU System distribution." <email@example.com> wrote:
> Hi Guix!
> I've recently contributed a few tools that make a few OSS machine
> learning programs usable for Guix, namely nerd-dictation for dictation
> and llama-cpp as a converstional bot.
> In the first case, I would also like to contribute parameters of some
> localized models so that they can be used more easily through Guix. I've
> already discussed this subject when submitting these patches, without a
> clear answer.
> In the case of nerd-dictation, the model parameters that can be used
> are listed here : https://alphacephei.com/vosk/models
> One caveat is that using all these models can take a lot of space on the
> servers, a burden which is not useful because no build step are really
> needed (except an unzip step). In this case, we can use the
> #:substitutable? #f flag. You can find an example of some of these
> packages right here :
> So my question is: Should we add this type of models in packages for
> Guix? If yes, where should we put them? In machine-learning.scm? In a
> new file machine-learning-models.scm (such a file would never need new
> modules, and it might avoid some confusion between the tools and the
> parameters needed to use the tools)?
> Best regards,
> Nicolas Graves
- Guidelines for pre-trained ML model weight binaries (Was re: Where should we put machine learning model parameters?),
Ryan Prior <=