Freely accessible large language models have accelerated the pace of innovation, computer scientists say.
In February, an even bigger push came for the open-source movement when Facebook’s parent company, Meta, made a model called LLaMA freely available to selected external developers. Within a week, the LLaMA code was leaked and published online for anyone to download.
Open-source developers have been experimenting with ways of shrinking LLaMA down even more. Some of these techniques involve keeping the number of parameters the same but reducing the parameters’ precision — an approach that, surprisingly, does not cause unacceptable drops in performance.
Shrinking down AI tools could help to make them more widely accessible, says Vukosi Marivate, a computer scientist at the University of Pretoria. For example, it could help organizations such as Masakhane, a community of African researchers led by Marivate that is trying to make LLMs work for languages for which there isn’t a lot of existing written text that can be used to train a model.
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