LoRA - Low-rank Adaption of AI Large Language Models: LoRA and QLoRA Explained Simply

Key Takeaways

what is Laura in AI you may have heard of a concept called Laura referring to Ai and large language models but what is it imagine you have a giant box of Legos you can build all kinds of things with this giant box houses cars spaceships but it's so big and heavy that it's hard to carry around and most of the time you don't need all these Legos to build what you want to build so instead you build a smaller box of your favorite most useful Legos this smaller box is easier to carry around and you can still build most of the things that you want in this analogy the giant box of Legos is like a large language model for example gpt4 it's powerful and can do lots of things but it's also big and heavy it requires a lot of computational resources to use the smaller box of Legos is like a low rank adaptation of the large language model it's a smaller lighter version of the model that's been adapted for a specific task it's not as powerful as the full model there might be some things that it can't do but it's more efficient and easier to use Laura stands for low rank adaptation low rank in this context referred to a mathematical technique used to create this smaller lighter model you can also think of low rank as just reading the highlighted parts of a book full rank would be reading the entire book and low rank would be reading just the important highlighted bits why is Laura important let's say you have a large and advanced AI model trained on recognizing all sorts of images you can fine tune it to do a related task like recognizing images of cats specifically you do that by making small adjustments to this large model you can also fine-tune it to add behaviors you want or remove behaviors you don't but this can be very expensive in terms of what computers you would need and how long it would take Laura solves this problem by making it cheap and fast to fine-tune these smaller models Laura is important because one efficiency using Laura can greatly reduce the amount of resources used to train AI models to perform these tasks two speed these lower ranked models are faster to train but also they can provide faster outputs this can be crucial and applications where results need to happen in real time three limited resources and many real world applications the devices that are available to run AI models may have limited computational power or memory your smartphone may not be able to run a large language model but a low rank adaptation can be used for specific tasks you may need for stacking and transferring low rank adaptations can be helpful for transfer learning were a model trained on one task can be adapted to a different but related task this is much more efficient than training the large model to do something from scratch the updates and new skills learned by these low rank adaptations can also stack with other such adaptations so multiple models can benefit each other as well as the original larger model Q Laura Q Laura is a similar concept the queue stands for quantized so keylora is quantized low rank adaptation quantized refers to data compression quantization is converting a continuous range of values into a finite set of possible values imagine if you're an artist mixing paint you have an almost infinite range of colors you can create by mixing different amounts of colors together this is like a continuous signal in the real world but if you're working with a computer Graphics program it can't handle an infinite range of colors it might only allow each color component red green and blue to have one of many levels of intensity this limited set of possible colors is like a quantized signal here it can apply to reducing the number of decimal places we need to express a number for example Pi is an infinitely long number but we can use 3.14 as an approximation when doing calculations I hope you enjoyed that take a look at our next video or check out our top watched videos in the description