LVL 01SK
Project overview
SYSTEM ARCHITECTURE

Tiny GPT From Scratch

Follow the data, decisions, feedback, and validation boundaries before writing the full system.

Tiny GPT From Scratch first-principles architecture infographic

How to read this diagram

Read left to right for the forward path: raw information becomes a representation, passes through the project’s main computational ideas, and produces an output that can be measured. Then follow the feedback path back toward the trainable or decision-making components.

01

Causal attention

Attention builds a content-dependent weighted average. Queries describe what each position needs, keys describe what each position offers, and values carry the information. Scaling by the square root of key dimension prevents dot products from pushing softmax into saturation.

Boundary check: document its accepted input, output shape, mutable state, failure modes, and the metric that proves this stage is correct before connecting it downstream.

02

Adam

Adam tracks exponential moving averages of gradients and squared gradients, applies bias correction early in training, and scales each parameter update by its estimated second moment. Epsilon placement and weight-decay semantics matter.

Boundary check: document its accepted input, output shape, mutable state, failure modes, and the metric that proves this stage is correct before connecting it downstream.

03

Sampling

Autoregressive modeling factorizes a sequence into next-token conditional probabilities. A causal mask blocks future information; temperature, top-k, and nucleus sampling reshape the distribution at inference without retraining.

Boundary check: document its accepted input, output shape, mutable state, failure modes, and the metric that proves this stage is correct before connecting it downstream.

Architecture review checklist

  • Every arrow has a documented shape, dtype, unit, or schema.
  • Training and evaluation paths cannot leak information into each other.
  • Randomness is seeded and captured in experiment metadata.
  • Expensive stages expose timing, memory, throughput, and error metrics.
  • Each feedback loop has a stop condition and a rollback strategy.
  • Small reference implementations exist for numerical comparisons.