poloclub/transformer-explainer
Transformer Explainer
This project is a collection of interactive graphical tools designed for monitoring neural network training, latent space mappings, and the internal mechanisms of transformers. It functions as a visual learning environment for understanding how large language models process tokens and an educational tool for analyzing the interactions between generators and discriminators within adversarial networks.
The system provides a browser-based transformer architecture visualizer to show the mathematical operations used for token prediction in real time. It also includes a generative adversarial network visualizer that tracks discriminator confidence heatmaps and generator manifold analysis.
The toolset covers machine learning interpretability through real-time gradient tracking and the visualization of high-dimensional data distributions. It includes training controls for managing iteration speed via step-by-step execution and the live tuning of hyperparameters to observe immediate effects on model behavior.
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.
