LLM4LLM Visualizations

Interactive demonstrations and visual explorations of language model concepts, from foundations to reasoning and alignment

Explore Language Models Interactively

This collection provides hands-on visualizations that help you understand how language models work, from basic word prediction to modern transformer architectures and beyond to reasoning and alignment with human values.

37+
Interactive Visualizations
3
Modules Available
20+
Key Concepts Covered
0
Prerequisites Required
All Topics
Embeddings
Attention
Training
Prediction
Architecture
Statistics
Tokenization
Scaling
Reasoning
Alignment
RLHF
1

Foundations of Word Prediction

From basic statistics to neural word embeddings

→ Explore Module 1
17
Visualizations
Session 1.1: Introduction to Next-Word Prediction
Explore how language follows statistical patterns and enables AI instruction following
📊
Word Frequency Explorer
Power Laws Statistics
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❓
Instruction Following Patterns
Emergent Behavior QA
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Session 1.2: N-gram Models and Their Limitations
Build n-grams and discover the sparsity problem that motivates neural approaches
🔤
N-gram Builder
N-grams Context
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🕳️
Sparsity Explorer
Sparsity Limitations
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Session 1.3: Neural Language Models
Understand word embeddings, softmax, and the first neural language models
🗺️
Word Embedding Space
Embeddings Semantics
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🧠
Bengio Neural Language Model
Architecture Neural Networks
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Session 1.4: Training Neural Language Models
Explore loss functions, gradient descent, and training dynamics
📉
Loss Function Explorer
Loss Functions Training
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⛰️
Gradient Descent Simulator
Optimization Gradient Descent
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📈
Training Progress Visualizer
Training Metrics
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🔢
Perplexity Calculator
Perplexity Evaluation
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Session 1.5: Word2Vec and Static Embeddings
Deep dive into Word2Vec architectures and discover their limitations
🔄
Word2Vec Architecture Comparison
Word2Vec Skip-gram CBOW
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🎯
Negative Sampling Demo
Negative Sampling Efficiency
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🧮
Vector Analogy Solver
Analogies Vector Math
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🎭
Polysemy Problem Demo
Limitations Context
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2

Transformer Architecture

Attention mechanisms, contextual embeddings, and modern LLMs

→ Explore Module 2
13+
Visualizations
Session 2.0: Generative Search Engines
Understanding the paradigm shift to generative systems
🔍
Search Engine vs Generative Search
Concepts Search Generation
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🏗️
Architecture Evolution
Evolution Bengio to Transformer Scaling
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Session 2.1: From Text to Transformer Inputs
Tokenization, knowledge storage, and the selection problem
✂️
Tokenization Explorer
Tokenization BPE Subword
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🧠
FFN Knowledge Storage
FFN Knowledge Storage
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🎛️
Selection Problem Demo
Selection Context Problems
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Session 2.2: Attention Mechanisms
Understanding attention weights and transformer blocks
👁️
Attention Weights Visualizer
Attention Weights Dynamic
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📍
Position Embeddings
Position Order Embeddings
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👥
Multi-Head Attention
Multi-Head Specialization Parallel
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🧱
Transformer Block Builder
Transformer Architecture Complete
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Session 2.3: Training at Scale
Scaling laws and supervised fine-tuning
📈
Scaling Laws Explorer
Scaling Power Laws Performance
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🎯
SFT Transformation Demo
SFT Fine-tuning
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3

Beyond Prediction - Reasoning and Alignment

From pattern matching to reasoning and human value alignment

→ Explore Module 3
6
Visualizations
Session 3.0: Beyond Prediction - Learning Without Labels
Why language models need reinforcement learning and how it enables new capabilities
🔄
Learning Paradigm Shift
Paradigm Shift RL vs Supervised Concepts
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Session 3.1: The Alignment Problem and RLHF
How reinforcement learning from human feedback transforms text predictors into helpful assistants
🎯
RLHF Pipeline Demo
RLHF Alignment Pipeline
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👥
Preference Learning Demo
Preference Learning Human Feedback Interactive
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Session 3.2: Beyond Pattern Matching to Reasoning
How models develop sophisticated reasoning through chain-of-thought and test-time computation
🔗
Chain-of-Thought vs Direct Prediction
Chain-of-Thought Reasoning Comparison
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⚡
Test-Time Computation Explorer
Test-Time Computation Dynamic Reasoning Interactive
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Session 3.3: From Prediction to Reasoning - The Complete Journey
Synthesis of the entire evolution from n-grams to sophisticated reasoning systems
📈
Evolution Timeline Interactive
Evolution Timeline Synthesis ML Principles
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Interactive visualizations for LLM4LLM - Educational materials for understanding language models

Open source and available on GitHub