Well, imagine you have a bunch of words in your head (like the ones on this page) and you want to organize them into sentences. That’s where these fancy algorithms come in! They help us figure out which words go together and make sense in context.

Now let me explain how it works with an example. Let’s say we have the sentence “The cat sat on the mat.” This is a pretty simple sentence, but if you break it down into its parts (like we do in linguistics), you get something like this:

(NP The) (VP cat) (PP sat) (on) (NP mat) .

Each of those parentheses represents a different part of speech. NP stands for noun phrase, VP is verb phrase, and PP is prepositional phrase. So basically, we’re breaking down the sentence into its smallest parts so that we can understand how they fit together.

Now let’s say we want to use LoftQ or QLoRA to help us figure out which words go together in this sentence. We would feed our algorithm a bunch of data (like all the sentences in English) and it would learn how to identify patterns and relationships between different parts of speech. For example, it might notice that “cat” is usually followed by “sat,” so it could predict that those two words are likely to go together in future sentences as well.

This is where quantization comes in basically, we’re breaking down the sentence into smaller and smaller pieces until we can understand how they fit together at a very basic level (like whether “cat” usually follows “the”). And by doing this, we can create more accurate language models that are better able to predict which words will go together in future sentences.

It might sound fancy, but at its core, it’s just a way of breaking down sentences into smaller pieces so that we can understand how they fit together better. And by doing this, we can create more accurate language models that are better able to predict which words will go together in future sentences.