Ashwin Labs Notes

part10_building_mini_gpt_conceptually

Building a Mini GPT From Scratch (Conceptually)

Part 10 of the Attention & Transformers Deep Dive Series

Introduction

Throughout this series, we explored:

  • Attention mechanisms
  • Self-attention
  • Transformer blocks
  • GPT-style generation
  • Training pipelines
  • Inference optimization
  • Memory systems
  • Agentic AI architectures

At this point, the theory should feel much less mysterious.

Now comes the most important step for truly internalizing Transformers:

mentally building one yourself.

This post walks through how a minimal GPT-style model is constructed conceptually.

The goal is not production-scale optimization. The goal is architectural clarity.

By the end, you should understand:

  • How the pieces fit together
  • What actually happens during forward passes
  • How text generation loops operate
  • Why Transformers scale so effectively

What “Mini GPT” Actually Means

We are NOT building:

  • GPT-4
  • A frontier-scale model
  • A production inference stack

We are building a simplified educational Transformer.

Even tiny GPT models still contain:

  • Embeddings
  • Positional encoding
  • Self-attention
  • Transformer blocks
  • Feed-forward networks
  • Autoregressive generation

The architecture principles remain the same.

High-Level Architecture

A minimal GPT pipeline looks like this:

Input Text
    ↓
Tokenization
    ↓
Embeddings
    ↓
Positional Encoding
    ↓
Transformer Blocks
    ↓
Linear Projection
    ↓
Softmax Probabilities
    ↓
Next Token Prediction

Everything we studied earlier now fits into one flow.

Step 1 — Tokenization

Input text:

"The cat sat"

becomes token IDs:

[10, 25, 81]

These are simply vocabulary indices.

The model still has no semantic understanding yet.

Step 2 — Embeddings

Each token ID maps into a learned vector.

Example:

embedding_dim = 128

Each token becomes:

[token] -> [128-dimensional vector]

Now the sequence becomes:

sequence_length × embedding_dimension

Example:

(3 × 128)

Step 3 — Positional Encoding

Transformers do not inherently understand sequence order.

So we inject position information.

Conceptually:

x = token_embedding + positional_embedding

Now semantic meaning and sequence position are combined.

Step 4 — Transformer Blocks

Now the real magic begins.

Each Transformer block contains:

Multi-Head Attention
        ↓
Residual + LayerNorm
        ↓
Feed Forward Network
        ↓
Residual + LayerNorm

This stack gets repeated many times.

Even tiny GPTs usually contain multiple Transformer layers.

Step 5 — Self-Attention

Inside attention:

  1. Embeddings become Q/K/V vectors
  2. Token similarities get computed
  3. Relevance weights emerge
  4. Contextual representations form

Core equation:

This enables:

  • Contextual understanding
  • Token relationships
  • Long-range dependencies

Step 6 — Causal Masking

GPT models must avoid cheating.

Future tokens remain hidden.

Attention matrix becomes:

✓ ✗ ✗ ✗
✓ ✓ ✗ ✗
✓ ✓ ✓ ✗
✓ ✓ ✓ ✓

This creates autoregressive generation behavior.

Step 7 — Feed Forward Networks

After attention each token independently passes through an FFN.

Typical structure:

Linear
  ↓
Activation
  ↓
Linear

This performs:

  • Nonlinear feature refinement
  • Representation expansion
  • Abstraction building

Step 8 — Final Projection Layer

Eventually the Transformer outputs contextual token representations.

These must become vocabulary probabilities.

A final linear layer projects hidden states into:

vocabulary_size

Example:

hidden_dim = 768
vocab_size = 50000

Final output shape:

768 → 50000

Now every token position produces probabilities across the vocabulary.

Step 9 — Softmax

Raw logits become probabilities.

Example:

TokenProbability
mat0.62
floor0.18
chair0.04

The model predicts the next token distribution.

Step 10 — Training Loop

Training repeatedly performs:

  1. Forward pass
  2. Loss calculation
  3. Backpropagation
  4. Parameter update

across massive datasets.

The objective remains:

next-token prediction.

Simplified Pseudocode

Very simplified conceptual flow:

for batch in dataset:
 
    tokens = tokenize(batch)
 
    embeddings = embedding_layer(tokens)
 
    x = embeddings + positional_embeddings
 
    for block in transformer_blocks:
        x = block(x)
 
    logits = output_projection(x)
 
    loss = cross_entropy(logits, targets)
 
    loss.backward()
 
    optimizer.step()

This is the core GPT training loop conceptually.

Step 11 — Generation Loop

Inference works differently.

Generation becomes iterative.

Example

Prompt:

"The cat"

Model predicts:

sat

Now prompt becomes:

"The cat sat"

Repeat.

Simplified Generation Pseudocode

tokens = tokenize(prompt)
 
while not stop_condition:
 
    logits = model(tokens)
 
    next_token = sample(logits)
 
    tokens.append(next_token)

This is fundamentally how GPT generates text.

Why KV Cache Matters Here

Without KV cache every generation step would recompute the entire sequence.

KV cache stores:

  • Previous Keys
  • Previous Values

dramatically accelerating inference.

Why Small GPTs Still Feel Impressive

Even tiny GPTs can:

  • Complete sentences
  • Generate coherent text
  • Mimic structure
  • Learn local reasoning patterns

because the Transformer architecture itself is extremely powerful.

Scale improves:

  • Capability
  • Robustness
  • Reasoning depth
  • Factuality

But the core mechanics remain similar.

What Tiny GPTs Usually Struggle With

Small models often struggle with:

  • Long-range reasoning
  • Factual consistency
  • Coding quality
  • Planning
  • Hallucination control

Large-scale capability emerges gradually with:

  • More parameters
  • More data
  • More compute

One Important Realization

Modern LLMs are not magical black boxes.

At a systems level, they are:

  • Stacked matrix operations
  • Probabilistic token predictors
  • Iterative representation refinement systems

The elegance comes from:

  • Scale
  • Optimization
  • Emergent structure

not from hidden symbolic logic engines.

Why Building One Changes Your Understanding

Reading about Transformers helps.

But mentally constructing one:

  • solidifies intuition
  • clarifies architecture flow
  • reveals bottlenecks
  • makes papers easier to read
  • improves systems thinking

This is often the moment when Transformers stop feeling mysterious.

What Comes After Mini GPTs

Once you understand mini GPTs conceptually, excellent next steps include:

Practical Engineering

  • Implement a tiny Transformer in PyTorch
  • Visualize attention maps
  • Experiment with token sampling
  • Build toy RAG systems

Systems Topics

  • Distributed training
  • Quantization
  • LoRA / PEFT
  • Inference serving
  • Batching systems

Research Topics

  • Mechanistic interpretability
  • Reasoning models
  • Multimodal architectures
  • Memory systems
  • World models

Final Thought

The Transformer architecture looks intimidating at first because many concepts interact simultaneously.

But underneath the complexity, the core flow is surprisingly elegant:

  1. Convert tokens into vectors
  2. Compare semantic relationships
  3. Exchange contextual information
  4. Refine representations repeatedly
  5. Predict likely next tokens

Scale that process across:

  • Enormous datasets
  • Massive compute
  • Deep architectures

and modern LLMs emerge.

That combination changed AI forever.

Next

⇒ Implementing Tiny Transformer in PyTorch

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