Transformers (how LLMs work) explained visually | DL5 | Highlights and Annotations by Gistr.

This segment introduces the core concept of transformers as a specific type of neural network crucial to the current AI boom. It explains that transformers are used in various applications, from audio transcription to image generation, and previews a step-by-step visual explanation of their inner workings. This segment details how large language models like ChatGPT generate text by repeatedly predicting the next word based on a probability distribution and sampling from it. It highlights the surprising effectiveness of this iterative prediction and sampling process, even though it might seem counterintuitive. This video explains the inner workings of transformers, the neural network architecture behind AI models like ChatGPT and DALL-E. It details the process of converting text into vectors, using attention mechanisms to understand context, and generating text through iterative prediction and sampling. The video emphasizes the role of matrix multiplications and the importance of word embeddings in capturing semantic meaning. It also covers the softmax function and the concept of temperature in controlling the randomness of text generation. This segment demonstrates the significant impact of model size on the quality of generated text. It compares the output of GPT-2 and GPT-3, showing how a larger model (GPT-3) produces much more coherent and contextually relevant text, even inferring meaning from abstract concepts. This segment provides a high-level overview of how data flows through a transformer. It explains the process of tokenization, vector embedding, attention blocks (where vectors interact and update their values based on context), and multi-layer perceptrons (where vectors undergo parallel transformations). This segment explains how a text prediction model can be adapted into a chatbot. It describes the process of using a system prompt to establish the context of a user interacting with an AI assistant and then iteratively predicting the AI's responses based on user input. This segment provides essential background on deep learning, explaining its core principles and the importance of its specific format for efficient training at scale. It emphasizes the use of arrays of real numbers (tensors) and weighted sums in deep learning models, setting the stage for understanding the inner workings of transformers.This segment delves into the structure of deep learning models, highlighting the use of tensors and matrix-vector products. It explains how model parameters (weights) interact with data through weighted sums, packaged as matrix-vector products, and introduces the concept of weights as the learned parameters that determine model behavior. This segment explains the process of converting words into vectors (word embeddings), a crucial first step in transformer-based text processing. It introduces the embedding matrix, a matrix whose columns determine the vector representation of each word in the model's vocabulary.This segment provides a geometric interpretation of word embeddings, explaining how words with similar meanings tend to cluster together in a high-dimensional space. It demonstrates how directions in this space can encode semantic meaning, using examples to illustrate how relationships between words can be captured by vector operations. This segment explains how word embeddings, initially representing single words, evolve within a neural network to incorporate contextual information, becoming nuanced representations influenced by surrounding words and phrases, mirroring human understanding of word meaning. The video discusses the concept of context size in transformer models, explaining how GPT-3's context size of 2048 limits the amount of text it can process simultaneously. This limitation is linked to the challenges of maintaining conversational coherence in long interactions with chatbots, illustrating the practical implications of this architectural constraint.This segment details the final prediction step in a transformer model. It explains how the model uses the last vector in the context to generate a probability distribution over all possible next words, highlighting the seemingly inefficient use of only the last vector while thousands of other context-rich vectors exist in the final layer. The explanation sets the stage for the discussion of training efficiency. The video defines "logits" as the raw, unnormalized outputs before the softmax function, emphasizing their importance in machine learning. The segment connects the preceding concepts to the upcoming discussion of the attention mechanism, highlighting the importance of a solid understanding of word embeddings, softmax, and dot products for grasping the attention mechanism. This section introduces the unembedding matrix, a crucial component in the model's architecture. It explains its function, its relationship to the embedding matrix, and its contribution to the overall parameter count of the network, providing a concrete example of the massive scale of these models. The segment also serves as a transition to the next topic.The explanation of the softmax function is provided here, detailing its role in transforming arbitrary numerical outputs into a valid probability distribution. The segment clarifies how softmax ensures that values are between 0 and 1 and sum to 1, making them suitable for representing probabilities of different outcomes.This segment introduces the temperature parameter in the softmax function, explaining how it influences the randomness of text generation. It demonstrates how different temperature values lead to varying degrees of predictability and originality in the generated text, ranging from trite outputs to nonsensical ones. The discussion also touches upon practical limitations imposed by APIs. 10-minute mark, the video explains: Input data for models must be formatted as an array of real numbers (often called a tensor). This input data is progressively transformed through multiple layers, each also structured as an array of real numbers. The final layer represents the model's output (e.g., probabilities for the next token in text processing). Model parameters are referred to as "weights". These weights interact with the data primarily through "weighted sums". Non-linear functions are also used but typically do not depend on parameters. I think it's kind of fun to reference the specific numbers from GPT-3 to count up exactly where those 175 billion come from.. even if nowadays there are bigger and better models,, this one has a certain charm as the first large-language model to really capture the world's attention outside of ML communities.. also,, practically speaking,, companies tend to keep much tighter lips around the specific numbers for more modern networks.. I just want to set the scene going in, that as you peek under the hood to see what happens inside a tool like ChatGPT, almost all of the actual computation looks like matrix vector multiplication.. there's a little bit of a risk getting lost in the sea of billions of numbers, but you should draw a very sharp distinction in your mind between the weights of the model, which I'll always color in blue or red, and the data being processed, which I'll always color in gray. the weights are the actual brains, they are the things learned during training, and they determine how it behaves.. the data being processed simply encodes whatever specific input is fed into the model for a given run, like an example snippet of text. with all of that as foundation, let's dig into the first step of this text processing example, which is to break up the input into little chunks and turn those chunks into vectors.. I mentioned how those chunks are called tokens, which might be pieces of words or punctuation, but every now and then in this chapter and especially in the next one, I'd like to just pretend that it's broken more cleanly into words. because we humans think in words, this will just make it much easier to reference little examples and clarify each step. the model has a predefined vocabulary, some list of all possible words, say 50,000 of them, and the first matrix that we'll encounter, known as the embedding matrix, has a single column for each one of these words. these columns are what determines what vector each word turns into in that first step. we label it we, and like all the matrices we see, its values begin random, but they're going to be learned based on data. turning words into vectors was common practice in machine learning long before transformers, but it's a little weird if you've never seen it before, and it sets the foundation for everything that follows, so let's take a moment to get familiar with it. we often call this embedding a word, which invites you to think of these vectors very geometrically as points in some high dimensional space. visualizing a list of three numbers as coordinates for points in 3d space would be no problem, but word embeddings tend to be much much higher dimensional. in GPT-3 they have 12,288 dimensions, and as you'll see, it matters to work in a space that has a lot of distinct directions. in the same way that you could GPT (Generative Pre-trained Transformer) models generate new text using a transformer neural network. They are pre-trained on vast datasets and fine-tuned for specific tasks. The transformer architecture uses an attention mechanism, allowing the model to weigh the importance of different words in a sequence. Input data is broken into tokens (word pieces or sub-word units), converted into vectors, and processed through multiple layers of attention and feed-forward networks. The attention mechanism enables the model to consider the context of each word when generating text. Vectors representing words are updated based on their relationships with other words in the sequence. The model's output is a probability distribution over all possible tokens, from which the next word is sampled. Temperature parameter controls the randomness of the prediction. The model's weights (parameters) are learned during training using backpropagation. GPT-3, for example, has 175 billion parameters organized into matrices. Word embeddings represent words as vectors in a high-dimensional space, where semantically similar words are closer together. The process involves multiple matrix multiplications and non-linear functions, culminating in a probability distribution over the vocabulary. Softmax function transforms the raw output into a probability distribution. The attention mechanism is a crucial component of the transformer architecture, enabling efficient processing of context and long-range dependencies in text.