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Pruna AI , a European startup that has been run on condensation algorithms for AI example , is making its optimization frameworkopen sourceon Thursday .
Pruna AI has been creating a framework that apply several efficiency methods , such as caching , pruning , quantization , and distillate , to a devote AI model .
“ We also standardize saving and loading the compressed poser , apply combination of these compressing method , and also evaluating your compressed mannikin after you compress it , ” Pruna AI co - fonder and CTO John Rachwan told TechCrunch .
In particular , Pruna AI ’s framework can evaluate if there ’s important tone expiration after compressing a model and the performance gain that you get .
“ If I were to practice a metaphor , we are similar to how Hugging Face exchangeable transformer and diffusers — how to call them , how to keep open them , dilute them , etc . We are doing the same , but for efficiency methods , ” he summate .
Big AI labs have already been using various compression methods already . For instance , OpenAI has been relying on distillation to create faster adaptation of its flagship models .
This is probable how OpenAI developed GPT-4 Turbo , a faster version of GPT-4 . Similarly , theFlux.1 - schnellimage generation model is a distilled adaptation of the Flux.1 model from Black Forest Labs .
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distillate is a proficiency used to extract knowledge from a with child AI good example with a “ teacher - student ” poser . Developers direct petition to a teacher model and record the outputs . Answers are sometimes equate with a dataset to see how precise they are . These outputs are then used to train the educatee model , which is trained to approximate the teacher ’s behavior .
“ For big troupe , what they ordinarily do is that they build this stuff in - sign . And what you may find in the open source world is normally ground on single method . For deterrent example , let ’s say one quantization method for LLM , or one lay away method acting for diffusion fashion model , ” Rachwan enunciate . “ But you may not find a tool that aggregates all of them , makes them all sluttish to employ and fuse together . And this is the heavy economic value that Pruna is land the right way now . ”
While Pruna AI supports any form of mannequin , from large language models to diffusion model , spoken language - to - textual matter models and computer imagination models , the company is focusing more specifically on epitome and picture generation framework right now .
Some of Pruna AI ’s live users includeScenarioandPhotoRoom . In addition to the open beginning edition , Pruna AI has an enterprise offering with advanced optimization feature , including an optimisation agent .
“ The most exciting feature that we are releasing soon will be a concretion federal agent , ” Rachwan said . “ Basically , you give it your model , you say : ‘ I require more speed but do n’t drop my accuracy by more than 2 % . ’ And then , the agent will just do its magic trick . It will find the best combination for you , give it for you . You do n’t have to do anything as a developer . ”
Pruna AI charges by the hour for its pro version . “ It ’s similar to how you would think of a GPU when you rent a GPU on AWS or any swarm service , ” Rachwan said .
And if your mannikin is a critical part of your AI infrastructure , you ’ll end up saving a lot of money on illation with the optimise example . For example , Pruna AI has made a Llama model eight times smaller without too much personnel casualty using its condensation framework . Pruna AI hopes its customers will think about its compression framework as an investment that pay up for itself .
Pruna AI call down a $ 6.5 million seed financial support round a few month ago . investor in the startup include EQT Ventures , Daphni , Motier Ventures , and Kima Ventures .
