AI Systems · advanced
Multi-Head Attention
Parallel heads, head_dim split, output projection.
Mental model
Attention lets each token in a sentence look at every other token and weight how relevant each one is. "Multi-head" runs several of these attention passes in parallel so the model can track different kinds of relationships at once (e.g. grammar, references, position).
How to study Multi-Head Attention
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use Stanford CS336 — L3 Architectures & hyperparameters (Spring 2025), CS224n — Self-Attention & Transformers (Stanford), The Illustrated Transformer to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Multi-Head Attention with Transformer Block. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Split embedding across heads and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Learn from primary sources
Practice and explain it back
Split embedding across heads
d_model=512, h=8. What is head_dim? If you project Q to 512 dims total, how many dims per head?
Expected evidence: head_dim = 512/8 = 64.
Open the interactive drill →Review prompts
- Multi-head attention splits d_model into h heads rather than running h full-width attentions. What does that buy?
Build evidence
Synthesize: AI Models & Training
Move from transformer foundations through pre-training, fine-tuning, post-training, compression, and evaluation. Produce one working system, benchmark, or evidence-backed design that integrates the path.
- Implements or precisely models the core mechanisms from all three milestones
- Includes at least one injected failure or adversarial case and demonstrates recovery
- Reports quality, latency, resource, reliability, or usability measurements relevant to the domain
- Ships a concise architecture note explaining decisions, trade-offs, and remaining risks