LLM Interactive Architecture
Build a transformer from scratch - an interactive, end-to-end guide to understanding how Large Language Models work from raw text to generated output.
What is a Large Language Model? 🤖
A Large Language Model (LLM) like ChatGPT is, at its core, a next-token prediction machine. Given a sequence of text, it predicts the most probable next word or subword by learning statistical patterns from massive datasets.
Under the hood, an LLM is built from a transformer architecture (Vaswani et al., 2017). The transformer's key innovation is self-attention: a mechanism that lets every word in a sequence attend to every other word to understand context.
In this interactive laboratory, you build every component of a working LLM from scratch. The model is lightweight (~550K parameters vs. GPT-3's 175B), but the underlying architecture is identical.
How This Live Laboratory Works 🧭
This is not a static tutorial - it is a live laboratory. A real PyTorch model runs on the backend. When you type text, it flows through actual tensor pipelines. When you train, real gradients update weights.
Work through Steps 1–10 in order. Each step explains the math, provides interactive visualizers, and offers reference PyTorch code. Explore Step 11 (Modern Architectures) to see how frontier models like LLaMA 3 and DeepSeek-V3 evolved.
Live Model Architecture 🏗️
| Embedding Dimension (d_model) | 64 |
| Attention Heads (n_heads) | 4 (16D per head) |
| Transformer Layers (n_layers) | 4 Blocks |
| Context Window Length | 64 Tokens |
| Vocabulary Size (V) | 256 (Tied Weights) |
| Dropout Rate | 0.1 |
| Total Parameters | ~550,000 |
Training & Dataset Status 📊
| Dataset Corpus | Shakespeare + Hafez (Bilingual) |
| Total Training Steps | 2,000 Steps |
| Batch Size | 32 |
| Initial Loss | ln(256) ≈ 5.545 |
| Converged Loss | ~1.82 |
| Learned BPE Merges | 416 Merges |
| Token Compression Ratio | 1.87× |
Architecture Roadmap
Step through each mathematical transformation from raw tokens to probabilities.
Tokenization (BPE)
Transform raw text into integer token IDs using Byte Pair Encoding (BPE).
Token Embeddings
Convert discrete token IDs into 64-dimensional continuous vectors capturing semantic meaning.
Positional Encoding
Inject geometric order into permutation-invariant self-attention representations.
Self-Attention
Allow each token to dynamically attend to every other preceding token via scaled dot-product.
Multi-Head Attention
Split 64D embeddings across 4 specialized 16D attention heads for diverse feature subspace learning.
Transformer Block
Combine Pre-LayerNorm, Multi-Head Attention, residual connections, and FeedForward MLP.
Full GPT Model
Final LayerNorm, tied de-embedding projection W_u = W_e, and vocabulary logits.
Training Loop
Cross-entropy loss calculation, perplexity metrics, and AdamW gradient descent convergence.
Backpropagation
Mathematical derivation of tied-weight gradients, unobserved token dynamics, and loss backpropagation.
Live Generation
Autoregressive next-token sampling loop with temperature scaling and nucleus sampling.
Modern Architectures
Frontier upgrades: RoPE, RMSNorm, SwiGLU, GQA, MLA, and Mixture of Experts (MoE).