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.

Get support from the community! Ask questions, share prompts, discuss benchmarks, get hyped at the latest and greatest model releases! Enjoy talking about our awesome hobby.

As ambassadors of the self-hosting machine learning community, we strive to support each other and share our enthusiasm in a positive constructive way.

Rules:

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.

Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they're still using the same algorithms since <over 10 years ago>.

Rule 4 - No implying that models are devoid of purpose or potential for enriching peoples lives.

founded 3 years ago

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pax@sh.itjust.works

noneabove1182@sh.itjust.works

Smokeydope@lemmy.world

MonsterBug@sh.itjust.works

how many models have you collected locally? (lemmy.ml)

submitted 5 days ago by leanleft@lemmy.ml to c/localllama@sh.itjust.works

6 comments fedilink hide all child comments

llm's are random text generators. each one is a different architecture, different training data, or different training recipe.

first off: you probably dont need to fill up your harddrive with every good model in existance. often a url link is sufficient.(unless the owner deletes it. but quants are often still there)

optional second questions: how many links do you have to decent models(excluding low value experiments) how many would you estimate that you have?

optional third question: how much is your divirsity of model usage? assuming you had the compute, how many tested models would you utilize for a single query across different models? average. max.

fourth question: do you still rely on inference compute for non-locally hosted models?

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[–] Smokeydope@lemmy.world 1 points 3 days ago* (last edited 3 days ago)

Local llms would be big boot to your privacy if nothing else. You dont know what cloud llm providers do with your info, how long they store your data, or who they sell it to. The only HIPPA compliant model I know of is Claude series through api key access specifically. With local llms you dont have to worry about those things.

Also consider they changing and tweaking models all the time behind the scenes what works great for you now may change next update. Once you download a local llm its yours no chance of changes happening to important therapy not.

Chain of thought models are really good for use cases where you want quality answers over speedy ones.