Tokenization and vocabulary management: Quick Reference — Data Preparation (NVIDIA-Certified Professional: Generative AI LLMs)

Tokenization and Vocabulary Management: Quick Reference

This quick reference covers essential facts and definitions for tokenization and vocabulary management in the context of preparing data for large language models (LLMs) as required for the NVIDIA-Certified Professional: Generative AI LLMs certification.

1. Tokenization Basics

• Token: The smallest unit of text input to an LLM, which can be a word, subword, character, or byte.
• Tokenization: The process of converting raw text into tokens for model ingestion.
• Types of Tokenizers:
• Word-level: Splits text by whitespace or punctuation; simple but large vocabulary.
• Subword-level: Uses subword units (e.g., Byte-Pair Encoding, WordPiece) to balance vocabulary size and coverage.
• Character-level: Splits text into individual characters; smallest units but longer sequences.

2. Key Tokenization Rules

• Maintain consistency: Use the same tokenizer and vocabulary across training and inference.
• Handle out-of-vocabulary (OOV) tokens by breaking them into known subwords or using special tokens.
• Preserve important tokens such as punctuation and special symbols if relevant to the task.
• Normalize text before tokenization (e.g., lowercasing, Unicode normalization) as per model requirements.

3. Vocabulary Management

• Vocabulary: The set of unique tokens recognized by the tokenizer.
• Vocabulary Size: Trade-off between model capacity and efficiency; typically ranges from 20,000 to 50,000 tokens for LLMs.
• Special Tokens: Include tokens for padding (<pad>), unknown words (<unk>), start/end of sequence (<sos>, <eos>), and separators.
• Vocabulary Pruning: Removing rare tokens to reduce size but may increase OOV rate.

4. Practical Tips

• Use pretrained tokenizer vocabularies when fine-tuning existing LLMs to ensure compatibility.
• For custom training, build vocabulary from representative corpus using frequency thresholds.
• Evaluate tokenizer performance by measuring tokenization efficiency and impact on downstream tasks.
• Document tokenizer and vocabulary details explicitly for reproducibility.

Worked Example: Tokenizing the Sentence "NVIDIA GPUs accelerate AI training."

• Raw text: "NVIDIA GPUs accelerate AI training."
• After normalization (lowercase): "nvidia gpus accelerate ai training ."
• Using subword tokenizer (e.g., BPE):
• Tokens: ["n", "##vidia", "gp", "##us", "accelerate", "ai", "training", "."]
• Vocabulary includes subwords like "n", "##vidia", "gp", "##us" to handle rare or compound words.

More in this topic

Tokenization and vocabulary management — Data Preparation (NVIDIA-Certified Professional: Generative AI LLMs)Tokenization and vocabulary management: Practice Questions — Data Preparation (NVIDIA-Certified Professional: Generative AI LLMs)Cleaning, curating, and organizing datasets — Data Preparation (NVIDIA-Certified Professional: Generative AI LLMs)Tokenization and vocabulary management: Worked Example — Data Preparation (NVIDIA-Certified Professional: Generative AI LLMs)Tokenization and vocabulary management: Common Mistakes — Data Preparation (NVIDIA-Certified Professional: Generative AI LLMs)Data Preparation — NVIDIA-Certified Professional: Generative AI LLMs