LocalLLaMA
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.
view the rest of the comments
I've been looking forward to trying this one.
I have some use cases where I need to do some large scale data cleanup, but using an LLM is overkill and I already get good results with smaller embeddings.
I want to try using this model and taking advantage of the Matryoshka dimension reduction to manage the progressively more complex use cases.
Not really sure I understand how these work, do you just feed it a large textual document like a transcript or something, and it turns it into a more machine readable vector format or something?
Or is it just a much smaller LLM that's more optimized for reading than generating?
Basically yes
I’ve only built my own systems that use sentence transformers
You pass in a list of strings, it generates a list of vectors, those vectors can be used for all sorts of similarity analysis and retrieval.