Understanding Tokenizers and Embedders in LLM Pipelines

Tokenizers and embedders are foundational components in language models. They transform raw text into machine-understandable representations and are pivotal for model performance.

1. Tokenizer Structure

Tokenizers map raw text to token IDs, enabling LLMs to process language.

Tokenizers are rule-based or algorithmically trained components that map raw text to token IDs. They are not neural networks but rely on predefined vocabularies and rules.

Typical Components

Vocabulary

A dictionary mapping tokens (e.g., words, subwords) to integer IDs.
Example: {"hello": 7592, "world": 2088, "[UNK]": 100}

Tokenization Algorithms

• Byte-Pair Encoding (BPE): Used in GPT models
• WordPiece: Used in BERT
• SentencePiece: Used in T5 and LLaMA
• Unigram: Used in ALBERT

Special Tokens

Markers like [CLS], [SEP], [PAD], and [MASK] for task-specific formatting.

2. Embedder (Embedding Layer) Structure

Embedders are neural network layers that map token IDs to dense vectors. They transform discrete IDs into continuous semantic vectors.

Components

1. Token Embeddings

   • A matrix of size [vocab_size, hidden_dim]
   • Each row represents a token’s embedding

2. Positional Embeddings

   • Encodes token positions in a sequence
   • Uses either learned embeddings (BERT) or sinusoidal functions

3. Segment Embeddings

   • Optional component
   • Distinguishes between sequences in tasks like question answering

3. Workflow for Training an LLM

Here’s an overview of the entire process, from tokenizer training to embedding learning:

1. Train the tokenizer on a large corpus to create vocabulary and splitting rules
2. Initialize the embedding layer using the pretrained tokenizer’s vocabulary
3. Train the entire LLM (including the embedding layer) on large-scale tasks
4. Fine-tune the embeddings on task-specific datasets

4. Practical Tips

Important: Always use the same tokenizer as the pretrained model to ensure consistent token-ID mappings.

5. Why Tokenizers and Embedders Are Key

Tokenizers convert raw text into discrete representations, while embedders transform them into continuous semantic vectors. Together, they form the foundation of any modern LLM, making efficient and meaningful language modeling possible.

Key Takeaways

• Tokenizers are trained separately and frozen after training
• Embedding layers are learned alongside the LLM during pretraining
• Mismatched tokenizers lead to invalid token-ID mappings
• Subword tokenization reduces out-of-vocabulary (OOV) issues