Understanding LoRA and QLoRA: Low-Rank Adaptation in AI-Language Model
A few Examples of the Lora Model in Image Pipeline
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What is LoRa in AI? You may have heard of a concept called LoRa, 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, GPT 4. 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.
LoRa 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 LoRa 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 rank models are faster to train, but also they can provide faster outputs. This can be crucial in applications where results need to happen in real time. Three, limited resources. In 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. 4. Stacking and Transferring Low rank adaptations can be helpful for transfer learning, where 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, QLORA. QLORA is a similar concept.
The Q stands for Quantized. So QLORA 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. Thanks for reading till here, have a great day.
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