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Now after exploring the data that you'll be using, in this lesson you'll learn about how to prepare that data for training. All right, let's jump into it. So next on what kind of data you need to prep, well there are a few good best practices. So one is you want higher quality data and actually that is the number one thing you need for fine-tuning rather than lower quality data. What I mean by that is if you give it garbage inputs, it'll try to parrot them and give you garbage outputs. So giving really high quality data is important. Next is diversity. So having diverse data that covers a lot of aspects of your use case is helpful. If all your inputs and outputs are the same, then the model can start to memorize them and if that's not exactly what you want, then the model will start to just only spout the same thing over and over again. And so having diversity in your data is, is really important. Next is real or generated. I know there are a lot of ways to create generated data, and you've already seen one way of doing that using an LLM, but actually having real data is very, very effective and helpful most of the time, especially for those writing tasks. And that's because generated data already has certain patterns to it. You might've heard of some services that are trying to detect whether something is generated or not. And that's actually because there are patterns in generated data that they're trying to detect. And as a result, if you train on more of the same patterns, it's not going to learn necessarily new patterns or new ways of framing things. And finally, I put this last because actually in most machine learning applications, having way more data is important than less data. But as you actually just seen before, pre-training handles a lot of this problem. Pre-training has learned from a lot of data, all from the internet. And so it already has a good base understanding. It's not starting from zero. And so having more data is helpful for the model, but not as important as the top three and definitely not as important as quality. So first, let's go through some of the steps of collecting your data. So you've already seen some of those instruction response pairs. So the first step is collect them. The next one is concatenate those pairs or add a prompt template. You've already seen that as well. The next step is tokenizing the data, um, adding padding or truncating the data. So it's the right size going into the model and you'll see how to tokenize that in the lab. So the steps to prepping your data is one collecting those instruction response pairs. Maybe that's question answer pairs, and then it's concatenating those pairs together, adding some prompt template, like you did before. The third step is tokenizing that data. And the last step is splitting that data into training and testing. Now in tokenizing, what, what does that really mean? Well, tokenizing your data is taking your text data and actually turning that into numbers that represent each of those pieces of text. It's not actually necessarily by word. It's based on the frequency of, you know, common character occurrences. And so in this case, one of my favorites is the ING token, which is very common in tokenizers. And that's because that happens in every single gerund. So in here, you can see finetuning, ING. So every single, you know, verb in the gerund, you know, fine-tuning or tokenizing all has ING and that maps onto the token 278 here. And when you decode it with the same tokenizer, it turns back into the same text. Now there are a lot of different tokenizers and a tokenizer is really associated with a specific model for each model as it was trained on it. And if you give the wrong tokenizer to your model, it'll be very confused because it will expect different numbers to represent different sets of letters and different words. So make sure you use the right tokenizer and you'll see how to do that easily in the lab. Cool, so let's head over to the notebook. Okay, so first we'll import a few different libraries and actually the most important one to see here is the AutoTokenizer class from the Transformers library by HuggingFace. And what it does is amazing. It automatically finds the right tokenizer or for your model when you just specify what the model is. So all you have to do is put the model and name in, and this is the same model name that you saw before, which is a 70 million Pythium base model. Okay, so maybe you have some text that says, you know, hi, how are you? So now let's tokenize that text. So put that in, boom. So let's see what encoded text is. All right, so that's different numbers representing text here. Tokenizer outputs a dictionary with input IDs that represent the token, so I'm just printing that here. And then let's actually decode that back into the text and see if it actually turns back into hi, how are you? Cool, awesome, it turns back into hi, how are you, so that's great. All right, so when tokenizing, you probably are putting in batches of inputs, so let's just take a look at a few different inputs together, so there's hi, how are you, I'm good, and yes. So putting that list of text through, you can just put it in a batch like that. Into the tokenizer, you get a few different things here. So here's hi, how are you again. I'm good, it's smaller. And yes, it's just one token. So as you can see, these are varying in length. Actually, something that's really important for models is that everything in a batch is the same length, because you're operating with fixed size tensors. And so the text needs to be the same. So one thing that we do do is something called padding. Padding is a strategy to handle these variable length encoded texts. Um, and for our padding token, you have to specify, you know, what you want to, what number you want to represent for, for padding. And specifically we're using a zero, which is actually the end of sentence token as well. So when we run, padding equals true through the tokenizer, you can see the yes string has a lot of zeros padded there on the right, just to match the length of this hi, how are you string. Your model will also have a max length that it can handle and take in so it can't just fit everything in and you've played with prompts before and you've noticed probably that there is a limit to the prompt length and so this is the same thing and truncation is a strategy to handle making those encoded text much shorter and that fit actually into the model so this is one way to make it shorter so as you can see here I'm just artificially changing the max length to three, setting truncation to true, and then seeing how it's,much shorter now, for hi, how are you? It's truncating from the right, so it's just getting rid of everything here on the right. Now, realistically, actually one thing that's very common is, you know, you're writing a prompt, maybe you have your instruction somewhere,and you have a lot of the important things maybe on the other side,on the right and that's getting truncated out. So, you know, specifying truncation side to the left actually can truncate it the other way. So this really depends on your task. And realistically for padding and truncation, you want to use both. So let's just actually set both in there. So truncation's true and padding's true here. I'm just printing that out so you can see the zeros here, but also getting truncated down to three. Great, so that was really a toy example. I'm going to now paste some code that you did in the previous lab on prompts. So here it's loading up the data set file with the questions and answers, putting it into the prompt, hydrating those prompts all in one go. So now you can see one data point here of question and answer. So now you can run this tokenizer on just one of those data points. So first concatenating that question with that answer and then running it through the tokenizer. I'm just returning the tensors as a NumPy array here just to be simple and running it with just padding and that's because I don't know how long these tokens actually will be, and so what's important is that I then figure out, you know, the minimum between the max length and the tokenized inputs. Of course, you can always just pad to the longest. You can always pad to the max length and so that's what that is here. And then I'm tokenizing again with truncation up to that max length. So let me just print that out. And just specify that in the dictionary, and cool. So that's what the tokens look like. All right, so let's actually wrap this into a full-fledged function so you can run it through your entire data set. So this is, again, the same things happening here that you already looked at, grabbing the max length, setting the truncation side. So that's a function for tokenizing your data set. And now what you can do is you can load up that dataset. There's a great map function here. So you can map the tokenize function onto that dataset. And you'll see here I'm doing something really simple. So I'm setting batch size to one, it's very simple. It is gonna be batched and dropping last batch true. That's often what we do to help with mixed size inputs. And so the last batch might be a different size. Cool. Great, and then so the next step is to split the data set. So first I have to add in this labels columns as for hugging face to handle it, and then I'm going to run this train test split function, and I'm going to specify the test size as 10% of the data. So of course you can change this depending on how big your data set is. Shuffle's true, so I'm randomizing the order of this data set. I'm just going to print that out here. So now you can see that the data set has been split across training and test set, 140 for a test set there. And of course this is already loaded up in Hugging Face like you had seen before, so you can go there and download it and see that it is the same. So while that's a professional data set, it's about a company, maybe this is related to your company for example, you could adapt it to your company. We thought that might be a bit boring, it doesn't have to be, so we included a few more interesting datasets that you can also work with and feel free to customize and train your models for these instead. One is for Taylor Swift, one's for the popular band BTS, and one is on actually open source large language models that you can play with. And just looking at, you know, one data point from the TayTay dataset, let's take a look. All right, what's the most popular. Taylor Swift song among millennials? How does this song relate to the millennial generation? Okay, okay. So, you can take a look at this yourself and yeah, these data sets are available via HuggingFace. And now in the next lab, now that you've prepped all this data, tokenized it, you're ready to train the model.