Understanding Transformers: A Visual Guide

The transformer architecture has revolutionized AI. Let's break it down piece by piece.

The Core Innovation: Self-Attention

At the heart of transformers lies the self-attention mechanism. Unlike RNNs that process sequences step by step, self-attention allows the model to look at all positions simultaneously.

How It Works

1. Query, Key, Value: Each input is transformed into three vectors
2. Attention Scores: Computed by taking the dot product of queries and keys
3. Weighted Sum: Values are combined based on attention scores

def self_attention(Q, K, V):
    scores = torch.matmul(Q, K.transpose(-2, -1))
    scores = scores / math.sqrt(d_k)
    attention = F.softmax(scores, dim=-1)
    return torch.matmul(attention, V)

Multi-Head Attention

Rather than performing attention once, transformers use multiple "heads" that can focus on different aspects of the input.

Position Encodings

Since attention is permutation-invariant, we need to inject positional information. This is done through sinusoidal or learned embeddings.

Why Transformers Scale

The key to transformer success is parallelization. Unlike RNNs, all positions can be computed simultaneously, making training on massive datasets feasible.

Conclusion

Understanding transformers is essential for anyone working in modern AI. They form the backbone of:

• Large Language Models (GPT, Claude, Llama)
• Vision Transformers (ViT)
• Multimodal models (CLIP, Flamingo)

Next up: Fine-tuning strategies for large language models.