Project 1: Build a Tokenizer Visualizer

Create an interactive tool that shows how different tokenizers split text, count tokens, and affect costs and context windows.

Quick Reference

Attribute	Value
Difficulty	Level 2: Intermediate
Time Estimate	6–10 hours
Language	Python or JavaScript
Prerequisites	Basic NLP concepts, CLI or web UI basics
Key Topics	BPE, WordPiece, token counts, context windows

Learning Objectives

By completing this project, you will:

Compare tokenization outputs across multiple models.
Visualize token boundaries including whitespace and special tokens.
Estimate costs from token counts.
Explain context window limits with concrete examples.
Export tokenization reports for benchmarking.

The Core Question You’re Answering

“How does tokenization change meaning, cost, and context capacity?”

If you can’t see tokens, you can’t control budgets or reliability.

Concepts You Must Understand First

Concept	Why It Matters	Where to Learn
Subword tokenization	Core to LLM input	HF tokenizers docs
Special tokens	Affect model behavior	Model docs
Context window limits	Defines memory capacity	LLM API docs
Cost per token	Budgeting inference	Provider pricing

Theoretical Foundation

Tokenization as a Lossy Encoding

Text -> Tokens -> IDs

Different tokenizers segment the same text differently. That changes:

token counts
model input length
cost and latency

Project Specification

What You’ll Build

A tool that accepts text and displays tokenization side-by-side for multiple models.

Functional Requirements

Tokenizer registry with selectable models
Token boundary rendering (visible whitespace)
Token counts + cost estimates
Comparison mode across 2–3 tokenizers
Export as JSON/CSV

Non-Functional Requirements

Deterministic outputs
Handles long inputs gracefully
Clear UI or CLI display

Real World Outcome

Example view:

Input: "Hello world!"

GPT-2 tokens: ["Hello", "Ġworld", "!"]  (3 tokens)
Llama tokens: ["▁Hello", "▁world", "!"] (3 tokens)
BERT tokens: ["hello", "world", "!"]   (3 tokens)

Export file includes:

{"model":"gpt2","count":3,"tokens":["Hello","Ġworld","!"]}

Architecture Overview

┌──────────────┐   text   ┌──────────────┐
│ Input UI/CLI │────────▶│ Tokenizers   │
└──────────────┘         └──────┬───────┘
                                ▼
                         ┌──────────────┐
                         │ Visualizer   │
                         └──────┬───────┘
                                ▼
                         ┌──────────────┐
                         │ Exporter     │
                         └──────────────┘

Implementation Guide

Phase 1: Tokenizer Loading (2–3h)

Load 2–3 HF tokenizers
Checkpoint: consistent tokenization output

Phase 2: Visualization (2–4h)

Render boundaries with visible whitespace
Checkpoint: long input remains readable

Phase 3: Metrics + Export (2–3h)

Add cost estimation and export
Checkpoint: JSON export validates

Common Pitfalls & Debugging

Pitfall	Symptom	Fix
Hidden whitespace	confusing output	render spaces explicitly
Wrong counts	mismatch with API	align tokenizer version
Slow UI	long input lag	paginate or truncate view

Interview Questions They’ll Ask

Why do token counts differ across models?
How does tokenization affect cost and latency?
What are special tokens and why do they matter?

Hints in Layers

Hint 1: Start with GPT-2 and BERT tokenizers.
Hint 2: Render whitespace tokens visibly.
Hint 3: Add model-specific cost estimates.
Hint 4: Export results for benchmarking.

Learning Milestones

Visible Tokens: boundaries shown clearly.
Comparable: multiple models side-by-side.
Actionable: cost estimates and exports available.

Submission / Completion Criteria

Minimum Completion

Tokenizer comparison output

Full Completion

Cost metrics + export

Excellence

Custom tokenizer training
Interactive UI

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