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GPT stands for Generative Pre-Training. First, it is a generative model, which can generate a new sample itself. For example, it can autocomplete a sentence or draw a new painting. Pre-Training is a very common trick in NLP. The model can train its weights on a large amount of unlabeled data and then further fine-tune the weights to specific tasks. Both BERT and GPT is trained as language modeling task. While BERT learns bi-directional representation for masked words, GPT aims to predict the next word given previous words. |
GPT is developed by OpenAI and has three generations: GPT-1, GPT-2 and GPT-3. The main difference is the model size. Model parameters have grown from 117 million in GPT-1, 1.5 billion in GPT-2 to 175 billion in GPT-3, making GPT-3 the largest neural network. |
Autoregressive Generative Model |
An autoregressive model predicts future values based on past values. The way the autoregressive generative NLP model actually work is that after each token is produced, that token is added to the sequence of inputs. And that new sequence becomes the input to the model in its next step. For example, the user initializes the input as "recite the first law $", where "$" is a special delimiter token. GPT model will generate the text autoregressively, conditioned on the user input. |
Architecture |
GPT is a trained Transformer decoder stack. To understand the Transformer encoder, please refer to my previous post: Step by Step into Transformer. It removes the encoder-decoder attention layer in the original Transformer and keeps the masked multi-headed self-attention layer and the feed forward layer. The output representations from a stack of encoders are then fed into text prediction task or a classification task. |
For some tasks, like text classification, we can directly fine-tune the model by adding a linear+softmax layer. Other tasks, like question answering or textual entailment, have structured inputs such as ordered sentence pairs, or triplets of document, question, and answers. GPT model uses a traversal-style approach, where we convert structured inputs into an ordered sequence that our pre-trained model can process. These input transformations allow us to avoid making extensive changes to the architecture across tasks. All transformations include adding initialized start token, a delimiter to separate multiple sentence inputs and end tokens. For example, for textual entailment or question answering, the inputs are transformed as one sequence, with premise and hypothesis sperated by the delimiter and surrounded by the Start and Extract tokens. We can also use multiple transformers and merge the outputs in the last layer for multiple choice tasks. |
Masked Self-Attention |
If you are not familiar with self-attention, I recommend to read my previous blog on Transformer: Step by Step into Transformer. The masked self-attention is pretty much the same with the self-attention except that it only attends to the previous tokens. So it is a one-directional self-attention rather than bi-directional as described in the Trasnformer. |
For example, in the following example, the token "it" attends to "a robot" and "it" in the previous token sequence on the left, since the right sequence is unobserved and to be generated. The output of the masked self-attention layer is the weighted average of the values of the tokens with the attention weights learned. |
For a more detailed illustration, we first calculate the attention scores through a product of queries and keys, as the same in self-attention. Then we apply an attention mask to make the upper right matrix a negative infinity. After applying the softmax function along rows, you can see that these negative infinity are zero out and thus, we only attend to the tokens on the left side, the lower bottom matrix including the diagnal. |
Use Cases |
Next, we will see how the same GPT architecture can be applied to solve various tasks without much change. |
Machine Translation |
During training, the pair of sentences are converted into an ordered sequence, separated by the delimiter <to-fr>. During scoring, the first sentence is input into the model, and it will auto-complete the following sentence, which is the corresponding translation. |
Summarization |
Given an article, the task is to generate a summary of the article. The model is trained to read a wikipedia article and summarize it. |
Music Generation |
MuseNet is a deep neural network that can generate 4-minute musical compositions with ten different instruments and combine styles from country to Mozart to the Beatles. MuseNet discovers harmony, rhythm, and style by learning to predict the next token in hundreds of thousands of MIDI files. MuseNet uses GPT-2, trained to predict the next token in a sequence, whether audio or text. |
Now in year 2024 It have new train yes i mean AI train in feature or now it will change erverything in life I think is it a lot Ok now We must understand What is GPT model How it work How to train it. |
What is GPT model |
GPT model is a large langue model. |
LLMs can be used for text generation, a form of generative AI, by taking an input text and repeatedly predicting the next token or word.Up to 2020, fine tuning was the only way a model could be adapted to be able to accomplish specific tasks. Larger sized models, such as GPT-3, however, can be prompt-engineered to achieve similar results. They are thought to acquire knowledge about syntax, semantics and "ontology" inherent in human language corpora, but also inaccuracies and biases present in the corpora. |
Some notable LLMs are OpenAI's GPT series of models I have NLP blogs from here check this out https://medium.com/gopenai/how-nlp-or-natural-language-processing-work-69772bddff0a |
released by OpenAI in 2020. Like its predecessor, GPT-2, it is a decoder-only transformer model of deep neural network, which supersedes recurrence and convolution-based architectures with a technique known as "attention". This attention mechanism allows the model to selectively focus on segments of input text it predicts to be most relevant.According to The Economist, improved algorithms, more powerful computers, and a recent increase in the amount of digitized material have fueled a revolution in machine learning. New techniques in the 2010s resulted in "rapid improvements in tasks", including manipulating language. Software models are trained to learn by using thousands or millions of examples in a "structure... loosely based on the neural architecture of the brain". One architecture used in natural language processing is a neural network based on a deep learning model that was introduced in 2017 - the transformer architecture. There are a number of NLP systems capable of processing, mining, organizing, connecting and contrasting textual input, as well as correctly answering questions. In esay to say It is NLP but is use a lot of langue data to train |
And How GPT model work the models are fed with an input sequence of words, and the model tries to find the most suitable next word by employing probability distributions to predict the most probable word or phrase. |
And how to training GPT model |
training GPT models |
GPT training refers to the process of supplying large volumes of text data to the model throughout the training phase to help it recognize patterns and connections between words, phrases and sentences in the text. The model employs deep learning algorithms to recognize patterns and correlations between words during training to comprehend and produce a language that resembles human speech. Training is a critical step in developing effective natural language processing models, as it allows the model to learn from vast amounts of data and improve its accuracy and efficiency on NLP-based tasks, such as language translation, text generation and question-answering. |
training GPT models |
GPT training refers to the process of supplying large volumes of text data to the model throughout the training phase to help it recognize patterns and connections between words, phrases and sentences in the text. The model employs deep learning algorithms to recognize patterns and correlations between words during training to comprehend and produce a language that resembles human speech. Training is a critical step in developing effective natural language processing models, as it allows the model to learn from vast amounts of data and improve its accuracy and efficiency on NLP-based tasks, such as language translation, text generation and question-answering. |
Data Gathering: The initial step in training a GPT model is to gather a lot of text data. Several sources can provide this information, including books, journals, and websites. The larger and more diverse the data, the better the model generates natural language text. |
Data Cleaning and Pre-processing: When the data has been gathered, it must be prepared by cleaning and preprocessing. To do this, remove extraneous data, including HTML elements, punctuation, and special characters. Also, for data simplification, it is divided into manageable chunks, such as words or subwords. |
Model Architecture: The GPT models use the Transformer architecture, which consists of a series of encoder and decoder layers. The decoder layers produce the output text, and the encoder layers process the input text. The model's size and number of layers may change depending on the task's difficulty. |
Pre-training: The model must be pre-trained on a significant amount of text input before it can be tailored for a particular purpose. The model is trained to anticipate the following word in a line of text as part of the pre-training phase. In order to do this, a random word from the sequence is taken out, and the model is trained to anticipate the missing word. |
Fine-tuning: Once the model has been pre-trained, it can be fine-tuned for a specific task, such as text classification or language translation. In order to do this, the model must be trained on a smaller dataset that is relevant to the given task. The model's parameters are changed during the fine-tuning procedure to increase its accuracy for the given task. |
Evaluation: After the model has been fine-tuned, it needs to be evaluated to ensure that it is performing well on the task. This involves testing the model on a separate dataset and measuring its performance using metrics such as accuracy or perplexity. |
Deployment: Once the model has been trained and evaluated, it can be deployed in a production environment where it can be used to generate natural language text for various applications. |
Thanks to Large Language Models, Artificial Intelligence has now caught the attention of pretty much everyone. ChatGPT, possibly the most famous LLM, has immediately skyrocketed in popularity due to the fact that natural language is such a, well, natural interface that has made the recent breakthroughs in Artificial Intelligence accessible to everyone. Nevertheless, how LLMs work is still less commonly understood, unless you are a Data Scientist or in another AI-related role. In this article, I will try to change that. |
Admittedly, that's an ambitious goal. After all, the powerful LLMs we have today are a culmination of decades of research in AI. Unfortunately, most articles covering them are one of two kinds: They are either very technical and assume a lot of prior knowledge, or they are so trivial that you don't end up knowing more than before. |
This article is meant to strike a balance between these two approaches. Or actually let me rephrase that, it's meant to take you from zero all the way through to how LLMs are trained and why they work so impressively well. We'll do this by picking up just all the relevant pieces along the way. |
This is not going to be a deep dive into all the nitty-gritty details, so we'll rely on intuition here rather than on math, and on visuals as much as possible. But as you'll see, while certainly being a very complex topic in the details, the main mechanisms underlying LLMs are very intuitive, and that alone will get us very far here. |
This article should also help you get more out of using LLMs like ChatGPT. In fact, we will learn some of the neat tricks that you can apply to increase the chances of a useful response. Or as Andrei Karparthy, a well-known AI researcher and engineer, recently and pointedly said: "English is the hottest new programming language." |
But first, let's try to understand where LLMs fit in the world of Artificial Intelligence. |
The field of AI is often visualized in layers: |
* Artificial Intelligence is very a broad term, but generally it deals with intelligent machines. |
* Machine Learning is a subfield of AI that specifically focuses on pattern recognition in data. As you can imagine, once you recoginze a pattern, you can apply that pattern to new observations. That's the essence of the idea, but we will get to that in just a bit. |
* Deep Learning is the field within ML that is focused on unstructured data, which includes text and images. It relies on artificial neural networks, a method that is inspired by the human brain. |
* Large Language Models deal with text specifically, and that will be the focus of this article. |
As we go, we'll pick up the relevant pieces from each of those layers. We'll skip only the most outer one, Artificial Intelligence and head straight into what is Machine Learning. |
The goal of Machine Learning is to discover patterns in data. Or more specifically, a pattern that describes the relationship between an input and an outcome. This is best explained using an example. |
Let's say we would like to distinguish between two of my favorite genres of music: reggaeton and R&B. If you are not familiar with those genres, here's a very quick intro that will help us understand the task. Reggaeton is a Latin urban genre known for its lively beats and danceable rhythms, while R&B is a genre rooted in African-American musical traditions, characterized by soulful vocals and a mix of upbeat and slower-paced songs. |
Suppose we have 20 songs. We know each song's tempo and energy, two metrics that can be simply measured or computed for any song. In addition, we've labeled them with a genre, either reggaeton or R&B. When we visualize the data, we can see that high energy, high tempo songs are primarily reggaeton while lower tempo, lower energy songs are mostly R&B, which makes sense. |
However, we want to avoid having to label the genre by hand all the time because it's time consuming and not scalable. Instead, we can learn the relationship between the song metrics and genre and then make predictions using only the readily available metrics. |
In Machine Learning terms, we say that this is a classification problem, because the outcome variable. |
We can now "train" a Machine Learning model using our labeled dataset, i.e., using a set of songs for which we do know the genre. Visually speaking, what the training of the model does here is that it finds the line that best separates the two classes. |
How is that useful? Well, now that we know this line, for any new song we can make a prediction about whether it's a reggaeton or an R&B song, depending on which side of the line the song falls on. All we need is the tempo and energy, which we assumed is more easily available. That is much simpler and scalable than have a human assign the genre for each and every song. |
Additionally, as you can imagine, the further away from the line, the more certain we can be about being correct. Therefore, we can often also make a statement on how confident we are that a prediction is correct based on the distance from the line. For example, for our new low-energy, low-tempo song we might be 98 percent certain that this is an R&B song, with a two percent likelihood that it's actually reggaeton. |
But of course, reality is often more complex than that. |
The best boundary to separate the classes may not be linear. In other words, the relationship between the inputs and the outcome can be more complex. It may be curved as in the image above, or even many times more complex than that. |
Reality is typically more complex in another way too. Rather than only two inputs as in our example, we often have tens, hundreds, or even thousands of input variables. In addition, we often have more than two classes. And all classes can depend on all these inputs through an incredibly complex, non-linear relationship. |
Even with our example, we know that in reality there are more than two genres, and we need many more metrics other than tempo and energy. The relationship among them is probably not so simple either. |
What I mainly want you to take away is this: The more complex the relationship between input and output, the more complex and powerful is the Machine Learning model we need in order to learn that relationship. Usually, the complexity increases with the number of inputs and the number of classes. |
In addition to that, we also need more data as well. You will see why this is important in just a bit. |
Let's move on to a slightly different problem now, but one for which we will simply try to apply our mental model from before. In our new problem we have as input an image, for example, this image of a cute cat in a bag. |
As for our outcome, let's say this time that we have three possible labels: tiger, cat, and fox. If you need some motivation for this task, let's say we may want to protect a herd of sheep and sound an alarm if we see a tiger but not if we see a cat or a fox. |
We already know this is again a classification task because the output can only take on one of a few fixed classes. Therefore, just like before, we could simply use some available labeled data and train a Machine Learning model. |
However, it's not quite obvious as to exactly how we would process a visual input, as a computer can process only numeric inputs. Our song metrics energy and tempo were numeric, of course. And fortunately, images are just numeric inputs too as they consist of pixels. They have a height, a width, and three channels. |
However, now we are facing two problems. First, even a small, low-quality 224x224 image consists of more than 150,000 pixels , but now we suddenly have at least 150,000. |
Second, if you think about the relationship between the raw pixels and the class label, it's incredibly complex, at least from an ML perspective that is. Our human brains have the amazing ability to generally distinguish among tigers, foxes, and cats quite easily. However, if you saw the 150,000 pixels one by one, you would have no idea what the image contains. But this is exactly how a Machine Learning model sees them, so it needs to learn from scratch the mapping or relationship between those raw pixels and the image label, which is not a trivial task. |
Let's consider another type of input-output relationship that is extremely complex - the relationship between a sentence and its sentiment. By sentiment we typically mean the emotion that a sentence conveys, here positive or negative. |
Let's formalize the problem setup again: As the input here we have a sequence of words, i.e., a sentence, and the sentiment is our outcome variable. As before, this is a classification task, this time with two possible labels, i.e., positive or negative. |
As with the images example discussed earlier, as humans we understand this relationship naturally, but can we teach a Machine Learning model to do the same? |
Before answering that, it's again not obvious at the start how words can be turned into numeric inputs for a Machine Learning model. In fact, this is a level or two more complicated than what we saw with images, which as we saw are essentially already numeric. This is not the case with words. We won't go into details here, but what you need to know is that every word can be turned into a word embedding. |
In short, a word embedding represents the word's semantic and syntactic meaning, often within a specific context. These embeddings can be obtained as part of training the Machine Learning model, or by means of a separate training procedure. Usually, word embeddings consist of between tens and thousands of variables, per word that is. |
To summarize, what to take away from here is that we can take a sentence and turn it into a sequence of numeric inputs, i.e., the word embeddings, which contain semantic and syntactic meaning. This can then be fed into a Machine Learning model. |
Great, but now we face the same challenges as with the visual input. As you can imagine, with a long sentence , we can quickly reach a very large number of inputs because of the large size of the word embeddings. |
The second problem is the relationship between language and its sentiment, which is complex - very complex. Just think of a sentence like "That was a great fall" and all the ways it can be interpreted. |
What we need is an extremely powerful Machine Learning model, and lots of data. That's where Deep Learning comes in. |
We already took a major step toward understanding LLMs by going through the basics of Machine Learning and the motivations behind the use of more powerful models, and now we'll take another big step by introducing Deep Learning. |
We talked about the fact that if the relationship between an input and output is very complex, as well as if the number of input or output variables is large , we need more flexible, powerful models. A linear model or anything close to that will simply fail to solve these kinds of visual or sentiment classification tasks. |
This is where neural networks come in. |
Neural networks are powerful Machine Learning models that allow arbitrarily complex relationships to be modeled. They are the engine that enables learning such complex relationships at massive scale. |
In fact, neural networks are loosely inspired by the brain, although the actual similarities are debatable. Their basic architecture is relatively simple. They consist of a sequence of layers of connected "neurons" that an input signal passes through in order to predict the outcome variable. You can think of them as multiple layers of linear regression stacked together, with the addition of non-linearities in between, which allows the neural network to model highly non-linear relationships. |
Neural networks are often many layers deep , which means they can be extremely large. ChatGPT, for example, is based on a neural network consisting of 176 billion neurons, which is more than the approximate 100 billion neurons in a human brain. |
So, from here on we will assume a neural network as our Machine Learning model, and take into account that we have also learned how to process images and text. |
Finally, we can start talking about Large Language Models, and this is where things get really interesting. If you have made it this far, you should have all the knowledge to also understand LLMs. |
What's a good way to start? Probably by explaining what Large Language Model actually means. We already know what large means, in this case it simply refers to the number of neurons, also called parameters, in the neural network. There is no clear number for what constitutes a Large Language Model, but you may want to consider everything above 1 billion neurons as large. |
With that established, what's a "language model"? Let's discuss this next - and just know that in a bit, we'll also get to learn what the GPT in ChatGPT stands for. But one step at a time. |
Let's take the following idea and frame it as a Machine Learning problem: What is the next word in a given sequence of words, i.e., in a sentence or paragraph? In other words, we simply want to learn how to predict the next word at any time. From earlier in this article we've learned everything we need to frame that as a Machine Learning problem. In fact, the task is not unlike the sentiment classification we saw earlier. |
As in that example, the input to the neural network is a sequence of words, but now, the outcome is simply the next word. Again, this is just a classification task. The only difference is that instead of only two or a few classes, we now have as many classes as there are words - let's say around 50,000. This is what language modeling is about - learning to predict the next word. |
Okay, so that's orders of magnitude more complex than the binary sentiment classification, as you can imagine. But now that we also know about neural networks and their sheer power, the only response to that concern is really "why not?" |
We know the task, and now we need data to train the neural network. It's actually not difficult to create a lot of data for our "next word prediction" task. There's an abundance of text on the internet, in books, in research papers, and more. And we can easily create a massive dataset from all of this. We don't even need to label the data, because the next word itself is the label, that's why this is also called self-supervised learning. |
The image above shows how this is done. Just a single sequence can be turned into multiple sequences for training. And we have lots of such sequences. Importantly, we do this for many short and long sequences so that in every context we learn what the next word should be. |
To summarize, all we are doing here is to train a neural network to predict the next word in a given sequence of words, no matter if that sequence is long or short, in German or in English or in any other language, whether it's a tweet or a mathematical formula, a poem or a snippet of code. All of those are sequences that we will find in the training data. |
If we have a large enough neural network as well as enough data, the LLM becomes really good at predicting the next word. Will it be perfect? No, of course not, since there are often multiple words that can follow a sequence. But it will become good at selecting one of the appropriate words that are syntactically and semantically appropriate. |
Now that we can predict one word, we can feed the extended sequence back into the LLM and predict another word, and so on. In other words, using our trained LLM, we can now generate text, not just a single word. This is why LLMs are an example of what we call Generative AI. We have just taught the LLM to speak, so to say, one word at a time. |
There's one more detail to this that I think is important to understand. We don't necessarily always have to predict the most likely word. We can instead sample from, say, the five most likely words at a given time. As a result, we may get some more creativity from the LLM. Some LLMs actually allow you to choose how deterministic or creative you want the output to be. This is also why in ChatGPT, which uses such a sampling strategy, you typically do not get the same answer when you regenerate a response. |
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PromeT90-1000 is a small data set for quickly training a model on high-quality data. All data has a minimum of 1000 words and a minimum quality score of 90%.
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