LocalLLaMA

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Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.

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Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.

Rule 2 - No comparing artificial intelligence/machine learning models to cryptocurrency. I.E no comparing the usefulness of models to that of NFTs, no comparing the resource usage required to train a model is anything close to maintaining a blockchain/ mining for crypto, no implying its just a fad/bubble that will leave people with nothing of value when it burst.

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NVIDIA's Peter Belcak Explains Why SLMs (smaller LLMs) are the Future of Agentic AI (www.youtube.com)

submitted 2 days ago by humanspiral@lemmy.ca to c/localllama@sh.itjust.works

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For coding AI, it could make sense to specialize models on architecture, functional/array split from loopy solutions, or just asking 4 separate small models, and then using a judge model to pick the best parts of each.

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[–] humanspiral@lemmy.ca 2 points 2 days ago* (last edited 2 days ago)

MoE

This optimization is about smaller matrix multiplications. Experts will specialize on input token types, and while it is better at being split accross resources (GPUs), it is not really specialization on "output domain" (type of work). All experts need to be in memory.

Deepseek made a 7b math focused LLM that beat all other models on math benchmarks, even 540b math specialist LLMs. More than any internal speed/structure "tricks", they achieved this through highly curated training data.

The small models we get now tend to just be pruned from larger generalist models. Paper/video is suggesting smaller models that are "large tuned" post trained to be domain specialists. Large models could select from domain specialist models and only load those in memory or act as a judge in combining outputs of "sub models"

Where an LLM is a giant probabilistic classifier, there are much faster/accurate/less compute intensive deterministic classifiers (expert/rule systems). Where SLMs have advantages, using even cheaper classification steps is going in the same direction. A smaller LLM is automatically a faster classifier, as a hammer to bang on everything alternative.