Build a Mini LLM from Scratch in NumPy: RoPE, GQA, SwiGLU Visual Guide
How to build a mini LLM from scratch in NumPy — RoPE, GQA, QK-Norm, SwiGLU, tied embeddings, and a 3.87M chat companion with full architecture visuals.
GPT Math Explained: The Full Forward Pass Beyond Attention
How does GPT math actually work end to end? Follow one training step from token IDs through Q/K/V, multi-head attention, MLP, logits, cross-entropy loss, backprop, and AdamW — past O = Attention(Q,K,V).
Positional Encoding Explained: How Transformers Learn Word Order
What is positional encoding in transformers? Why attention is order-blind, learned vs sinusoidal position embeddings, why we add instead of concatenate, and a from-scratch NumPy implementation.
Transformer from Scratch: The Full Forward Pass, Backprop, and Weight Update Math
One full transformer training step worked by hand — embeddings, positional encoding, attention, layer norm, the encoder-decoder, cross-entropy loss, backprop, and Adam.
How Attention Works in Transformers: Queries, Keys and Values
How does attention work in transformers? A visual, step-by-step explanation of the attention mechanism — queries, keys, values, the attention pattern, masking, the low-rank value trick, and multi-head attention — with the real GPT-3 parameter counts.
How GPT Actually Works: A Visual Guide to Transformers
How does a GPT actually work? A plain-English, visual walkthrough of transformers — tokens, word embeddings, why directions in vector space mean something, dot products, softmax and temperature — with the real GPT-3 numbers worked out.
Build GPT-2 from Scratch in PyTorch: A Full Walkthrough
How to build GPT-2 from scratch in PyTorch — tokenization, causal self-attention, transformer blocks, weight tying, and a working training loop — implemented step by step and trained locally (124M parameters).