Not just LLMs but all kinds of models are equivlant to freeware, aka the model itself and other essential bits for it to work. I won’t even call it source avaliable as there is no source.
Take redis as example. I can still go grab the source and compile a binary that works. This doesn’t applies on ML models.
Of course one can argue the training process isn’t determistic thus even with the exact training corpus, it can’t create the same model in terms of bits on mulitple runs. However, I would argue the same corpus provide the chance to train a model of similar or equivalent performance. Hence the openness of the training corpus is an absolute requirement to qualify a model being FOSS.
I’ve seen this said multiple times, but I’m not sure where the idea that model training is inherently non-deterministic is coming from. I’ve trained a few very tiny models deterministically before…
Not just LLMs but all kinds of models are equivlant to freeware, aka the model itself and other essential bits for it to work. I won’t even call it source avaliable as there is no source.
Take redis as example. I can still go grab the source and compile a binary that works. This doesn’t applies on ML models.
Of course one can argue the training process isn’t determistic thus even with the exact training corpus, it can’t create the same model in terms of bits on mulitple runs. However, I would argue the same corpus provide the chance to train a model of similar or equivalent performance. Hence the openness of the training corpus is an absolute requirement to qualify a model being FOSS.
I’ve seen this said multiple times, but I’m not sure where the idea that model training is inherently non-deterministic is coming from. I’ve trained a few very tiny models deterministically before…
You sure you can train a model deterministically down to each bits? Like feeding them into sha256sum will yield the same hash?
Yes of course, there’s nothing gestalt about model training, fixed inputs result in fixed outputs