Project 1: Build a Mini-Transformer from Scratch

Implement a small decoder-only transformer that can generate text, so you understand attention, masking, and training dynamics from first principles.

Quick Reference

Attribute Value
Difficulty Level 4: Expert
Time Estimate 2–3 weeks
Language Python
Prerequisites PyTorch basics, linear algebra, gradients
Key Topics self-attention, causal masking, tokenization, training loops

Learning Objectives

By completing this project, you will:

  1. Implement multi-head self-attention with causal masking.
  2. Build a tokenizer pipeline (basic BPE or word-level).
  3. Train a small language model end-to-end on a toy corpus.
  4. Diagnose training instability (loss spikes, divergence).
  5. Visualize attention to interpret model behavior.

The Core Question You’re Answering

“What exactly happens when a transformer ‘looks’ at tokens, and why does it work?”

This project removes the API layer and forces you to implement the mechanics directly.

Concepts You Must Understand First

Concept Why It Matters Where to Learn
Self-attention The core transformer primitive Attention Is All You Need
Causal masking Prevents peeking into the future Transformer basics
Tokenization Text becomes model input Karpathy tokenizer tutorial
Optimization dynamics Training stability Deep learning training guides

Theoretical Foundation

Decoder-Only Transformer Flow

Tokens -> Embeddings -> [Attention + MLP] x N -> Logits -> Sampling

Key properties:

  • Attention creates context-aware embeddings
  • Masking enforces autoregressive generation
  • Sampling controls creativity vs determinism

Project Specification

What You’ll Build

A minimal GPT-style model that can generate coherent text after training on a small dataset.

Functional Requirements

  1. Tokenizer + vocabulary builder
  2. Embedding + attention + MLP layers
  3. Causal mask for autoregressive behavior
  4. Training loop with checkpoints
  5. Sampling controls (temperature, top-k)

Non-Functional Requirements

  • Reproducible training with fixed seeds
  • Clear logging of loss/perplexity
  • Runs on CPU or single GPU

Real World Outcome

Example output after training:

Prompt: "To be or not"
Completion: "to be or not to be, that is the question..."

Attention visualization shows which tokens influence predictions.

Architecture Overview

┌──────────────┐  tokens  ┌──────────────┐
│ Tokenizer    │────────▶│ Transformer  │
└──────────────┘         └──────┬───────┘
                                ▼
                         ┌──────────────┐
                         │ Sampler      │
                         └──────────────┘

Implementation Guide

Phase 1: Tokenizer + Data (3–5h)

  • Implement word-level or BPE tokenizer
  • Checkpoint: text encodes/decodes correctly

Phase 2: Model Forward Pass (6–10h)

  • Implement attention + MLP blocks
  • Checkpoint: forward pass runs on toy batch

Phase 3: Training + Sampling (8–20h)

  • Train model and monitor loss
  • Checkpoint: text generation is coherent

Common Pitfalls & Debugging

Pitfall Symptom Fix
Loss explodes NaNs during training lower LR, add grad clip
Leaky mask model sees future tokens verify mask shape
Token mismatch gibberish outputs fix vocab consistency

Interview Questions They’ll Ask

  1. Why does self-attention scale poorly with sequence length?
  2. How does causal masking enable autoregressive generation?
  3. What training signals indicate underfitting vs overfitting?

Hints in Layers

  • Hint 1: Start with a tiny model (2 layers, 2 heads).
  • Hint 2: Add causal mask and verify output changes.
  • Hint 3: Track perplexity during training.
  • Hint 4: Visualize attention weights to debug behavior.

Learning Milestones

  1. Tokenizer Works: text encodes/decodes correctly.
  2. Model Runs: forward pass stable.
  3. Model Learns: generated text improves.

Submission / Completion Criteria

Minimum Completion

  • Model trains and generates text

Full Completion

  • Attention visualization
  • Checkpoints saved

Excellence

  • Rotary embeddings or improved tokenizer
  • Holdout perplexity report

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/GENERATIVE_AI_LLM_RAG_LEARNING_PROJECTS.md.