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In this lesson you'll learn about instruction fine-tuning, a variant of fine-tuning that enabled GPT-3 to turn into chat GPT and give it its chatting powers. Okay, let's start giving chatting powers to all our models. Okay, so let's dive into what instruction fine-tuning is. Instruction fine-tuning is a type of fine-tuning. There are all sorts of other tasks that you can do like reasoning, routing, copilot, which is writing code, chat, different agents, but specifically instruction fine tuning, which you also may have heard as instruction tune or instruction following LLMs, teaches the model to follow instructions and behave more like a chatbot. And this is a better user interface to interact with the model as we've seen with chat GPT. This is the method that turned. GPT-3 into chat GPT, which dramatically increased AI adoption from just a few researchers like myself to millions and millions of people. So for the data set for instruction following, you can use a lot that already exists readily available either online or specific to your company, and that might be FAQs, customer support conversations, or Slack messages. So it's really this dialogue dataset or just instruction response datasets. Of course, if you don't have data, no problem. You can also convert your data into something that's more of a question-answer format or instruction following format by using a prompt template. So here you can see, you know, a README might be able to come be converted into a question-answer pair. You can also use another LLM to do this for you. There's a technique called Alpaca from Stanford that uses chat GPT to do this. And of course, you can use a pipeline of different open source models to do this as well. Cool. So one of the coolest things about fine tuning, I think, is that it teaches this new behavior to the model. And while, you know, you might have fine tuning data on what's the capital of France, Paris, because these are easy question answer pairs that you can get. You can also generalize this idea of question answering to data you might not have given the model for your fine-tuning data set, but that the model had already learned in its pre-existing pre-training step. And so that might be code. And this is actually findings from the chat GPT paper where the model can now answer questions about code even though they didn't have question answer pairs about that for their instruction fine-tuning. And that's because it's really expensive to get programmers to go, you know, label data sets where they ask questions about code and write the code for it. So an overview of the different steps of fine-tuning are data prep, training, and evaluation. Of course, after you evaluate the model, you need to prep the data again to improve it. It's a very iterative process to improve the model. And specifically for instruction fine-tuning and other different types of fine-tuning, data prep is really where you have differences. This is really where you change your data, you tailor your data to the specific type of fine tuning, the specific task of fine tuning that you're doing. And training and evaluation is very similar. So now let's dive into the lab where you'll get a peek at the alpaca dataset for instruction tuning. You'll also get to compare models again that have been instruction tuned versus haven't been instruction tuned, and you'll get to see models of varying sizes here. So first importing a few libraries, the first one that is important is again this load data set function from the data sets library and let's load up this instruction tune data set and this is specifying the alpaca data set and again we're streaming this because it's actually a hefty fine-tuning data set not as big as the pile of course. I'm going to load that up and just like before with the pile, you're going to take a look at a few examples. All right, so unlike the pile, it's not just text and that's it. Here it's a little bit more structured, but it's not as, you know, clear-cut as just question-answer pairs. And what's really, really cool about, you know, this is that the authors of the alpaca paper, they actually had two prompt templates because they wanted the model to be able to work with two different types of prompts and two different types of tasks essentially and so one is you know an instruction following one where there is an extra set of inputs for example it the instruction might be add two numbers and the inputs might be first number is three the second number is four and then there's prompt templates without input which you can see in these examples sometimes it's not relevant to have an input so it doesn't have that so these are the prompt templates that are being used and so again very similar to before you'll just hydrate those prompts and run them across the whole data set. And let's just print out one pair to see what that looks like. Cool, so that's input output here and you know how it's hydrated into the prompt. So it ends with response and then it outputs this response here. Cool, and just like before, you can write it to a JSON lines file. You can upload it to HuggingFace hub if you want. We've actually loaded it up at Lamini slash Alpaca so that it's stable, you can go look at it there and you can go use it. Okay, great. So now that you have seen what that instruction following data set looks like, I think the next thing to do is just remind you again on this tell me how to train my dog to sit prompt on different models. So the first one is going to be this llama 2 model that is again not instruction tuned. We're gonna run that. Tell me how to train my dog to sit. Okay, it starts with that period again and just says this so remember that before and then now we're gonna compare this to again the instruction tuned model right here okay so much better it's actually producing different steps and then finally I just want to share chatGPT again just so you can have this comparison right here great okay so that. That is a much larger set of models, ChatGPT is quite large compared to the Llama2 models. Those are actually 7 billion parameter models, ChatGPT is rumored to be around 70 billion, so very large models. You're also going to explore some smaller models. So one is that 70 million parameter model. And here I'm loading up these models. This is not super important yet, you'll explore this a bit more later, but I'm going to load up two different things to process the data and then run the model. And you can see here, the tag that we have here is a "EleutherAI/Pythia/70m". This is a 70 million parameter model that has not been instruction tuned. I'm going to paste some code here. It's a function to run inference, or basically run the model on text. We will go through these different sections of what exactly is going on in this function throughout the next few labs. Cool. So this model hasn't been fine-tuned. It doesn't know anything specific about a company, but we can load up this, company dataset again from before. So we're going to give this model a question from this dataset, probably just, you know, the first sample from the test set, for example. And so we can run this here. The question is, can Lamini generate technical documentation or user manuals for software projects? And the actual answer is yes, Lamini can generate technical documentation and user manuals for software projects. And it keeps going. But the model's answer is, I have a question about the following. How do I get the correct documentation to work? A, I think you need to use the following code, et cetera. So it's quite off. Of course, it's learned English, and it got the word documentation in there. So it kind of understands maybe that we're in a question-answer setting, because it has. A there for answer. But it's clearly quite off. And so it doesn't quite understand this data set in terms of the knowledge, and also doesn't understand the behavior that we're expecting from it. So it doesn't understand that it's supposed to answer this question. Ok, so now compare this to a model that we've now fine-tuned for you, but that you're actually about to fine-tune for instruction following. And so that's loading up this model. And then we can run the same question through this model and see how it does. And it says, yes, lamani can generate technical documentation or user manuals for software projects, et cetera, and so this is just far more accurate than the one before and it's following that right behavior that we would expect. Okay great so now that you've seen what an instruction following model does exactly the next step is to go through what you saw a peak of which is that tokenizer how to prep our data so that it is available to the model for training.