poloclub/transformer-explainer

Features

• Generative Adversarial Networks - Provides an educational environment to study the interaction between generator and discriminator networks.
• Transformer Architecture Visualizations - Renders a live interactive browser-based model showing how internal transformer components and operations predict next tokens.
• GAN Visualizers - Provides a graphical representation of the interactions between generators and discriminators in adversarial networks.
• Runtime Hyperparameter Tuning - Allows updating model configuration variables in memory during runtime to observe immediate changes in learning behavior.
• Browser-Based Inference - Implements neural network inference and training directly within the web browser using JavaScript.
• Architecture Visualizations - Visualizes the internal components and mathematical operations within a transformer model during real-time token prediction.
• Training Monitoring Tools - Ships a real-time dashboard for observing hyperparameters, gradients, and model behavior during training.
• Hyperparameter Tuning - Supports adjusting model settings and training speeds on the fly to observe immediate effects on behavior.
• Model Gradient Visualizations - Provides real-time visualization of gradient movements to show how generator networks update to fool discriminators.
• Interactive Training Analysis - Uses step-by-step iteration and live rendering to perform detailed studies of the neural network training process.
• Manifold Viewers - Provides a visualization tool for analyzing high-dimensional data distributions and latent space mappings.
• Operation Visualizers - Offers an interactive browser-based tool for visualizing mathematical operations and internal mechanisms of Transformers.
• LLM Education - Serves as a visual learning environment for understanding how large language models process tokens and predict sequences.
• Latent Space Manifold Visualization - Represents high-dimensional latent space transformations as a visual distribution of synthetic samples.
• Training Gradient Visualizations - Maps mathematical weight updates to vector lines on a 2D plane to visualize sample movement during training.
• Deep Learning Interpretability - Analyzes internal states and gradient movements to interpret how the model learns specific data distributions.
• Step-by-Step Execution Controls - Implements slow-motion mode and manual step-by-step iteration to allow detailed analysis of the learning process.
• Live Runtime Tuning - Supports modifying model settings during the training process to observe immediate effects on learning behavior.
• Interaction Visualizers - Displays the real-time interactions between a generator and discriminator to illustrate how adversarial networks learn.
• Mapping Analyzers - Represents the transformation of random noise into synthetic samples using manifold visualization to show data density changes.
• Step Execution Engines - Provides a manual trigger mechanism to execute training steps individually for frame-by-frame analysis.
• Performance Heatmaps - Renders a 2D heatmap of the classification surface to visualize discriminator confidence levels.
• Confidence Heatmaps - Provides a color-coded grid to visualize discriminator classification confidence across a 2D input space.
• Learning and Prompt Engineering - Browser-based visualization of transformer architecture using a live model.
• Visualization and Analysis - Interactive tool for visualizing transformer model operations.

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