Transformers (how LLMs work) explained visually

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Understand Transformers: Learn how transformers, a type of neural network, power modern AI tools like ChatGPT.
Tokenization: Break input text into tokens (words or subwords) and convert them into numerical vectors for processing.
Attention Mechanism: Study how attention blocks allow tokens to interact and update their meanings based on context.
Multi-Layer Perceptrons (MLPs): Explore how MLPs process vectors in parallel to refine predictions.
Embedding and Unembedding: Use embedding matrices to convert tokens into vectors and unembedding matrices to generate predictions.
Softmax Function: Apply the softmax function to convert raw outputs into probability distributions for next-word predictions.
Temperature Parameter: Experiment with the temperature parameter to control the randomness of text generation.
Context Size: Be aware of the context size limitation (e.g., 2048 tokens in GPT-3) and its impact on long conversations.
Training with Backpropagation: Understand how backpropagation is used to train large models like GPT-3.
Matrix Multiplication: Recognize that most computations in transformers involve matrix multiplications with tunable weights.

Definition: GPT stands for Generative Pretrained Transformer, a model that generates text, learns from data, and uses transformers for processing.
Applications: Transformers are used in text-to-speech, image generation (e.g., DALL-E, MidJourney), and language translation.
Core Concept: Transformers predict the next word in a sequence by repeatedly sampling from a probability distribution.

Tokenization: Input text is broken into tokens (words or subwords) and converted into numerical vectors.
Attention Mechanism: Tokens interact through attention blocks, updating their meanings based on context.
Multi-Layer Perceptrons (MLPs): Vectors are processed in parallel through MLPs to refine predictions.
Output Generation: The final vector is transformed into a probability distribution over possible next tokens using the softmax function.

Backpropagation: Transformers are trained using backpropagation, adjusting weights to minimize prediction errors.
Parameter Count: GPT-3 has 175 billion parameters, organized into matrices that process input data.
Context Size: GPT-3 processes up to 2048 tokens at a time, limiting its ability to handle long conversations.

Embedding Matrix: Words are mapped to high-dimensional vectors, where similar words have similar vector representations.
Semantic Meaning: Directions in the embedding space encode semantic relationships (e.g., gender, plurality).
Dot Products: Dot products measure alignment between vectors, helping the model understand relationships like singular vs. plural.

Softmax Function: Converts raw outputs into probability distributions for next-word predictions.
Temperature Parameter: Controls the randomness of text generation, with higher temperatures allowing for more creative but less coherent outputs.

“A transformer is a specific kind of neural network, a machine learning model, and it’s the core invention underlying the current boom in AI.”
“The attention block is what’s responsible for figuring out which words in context are relevant to updating the meanings of which other words.”
“GPT-3 has 175 billion parameters, organized into just under 28,000 distinct matrices.”
“Word embeddings tend to settle on a set of embeddings where directions in the space have a kind of semantic meaning.”
“Softmax is the standard way to turn an arbitrary list of numbers into a valid probability distribution.”
“Temperature controls how much randomness is introduced into the text generation process.”
“The context size limits how much text the transformer can incorporate when making a prediction.”
“Most of the actual computation in transformers looks like matrix-vector multiplication.”
“The embedding matrix maps words to vectors, while the unembedding matrix maps vectors back to words.”
“Attention mechanisms allow the model to efficiently incorporate context from long distances.”

Text Autocompletion Tool: Build a tool that predicts and suggests the next word or sentence in a text.
Chatbot Development: Create a chatbot using transformer models like GPT-3 for customer support or personal assistance.
Language Translation App: Develop an app that translates text between languages using transformer-based models.
Story Generator: Design a system that generates creative stories based on a seed text input.
Semantic Search Engine: Build a search engine that understands the semantic meaning of queries using word embeddings.

Hitler: Mentioned in the context of word embeddings and semantic relationships.
Mussolini: Referenced in the same context as Hitler.
Kat: A name used in an example of word embeddings.