Follow text through validation, normalization, pre-tokenization, subword segmentation, token IDs, truncation, chunking, and detokenization.
Tokenization is a versioned pipeline, not a universal word splitter: bytes and Unicode forms are normalized or preserved, a pre-tokenizer sets merge boundaries, a vocabulary maps subwords or bytes to IDs, and truncation or chat templates alter the final model input.
The submitted report remains byte-identical. This separate review, checked 2026-08-26, narrows implementation claims and gives visitors a decision path before they generalize from a local result.
Output produced deterministically by the local runtime.
Useful model or subset that must not be mistaken for full conformance.
Conclusion that needs an exact implementation, trust system, model, parser, or human review.
| Question | What the local page can answer | What it does not establish | Next evidence step |
|---|---|---|---|
| How do generic segmentation choices change boundaries? | Use the educational comparison and chunk matrix. | Any named production tokenizer's exact output. | Run the immutable production tokenizer on the same preserved input. |
| Will the input fit a model context? | Only for the synthetic sequence and selected limit. | Actual vendor token count or chat-template overhead. | Measure with exact tokenizer and template artifacts. |
| Does a boundary change imply a security bypass? | No — it identifies a differential to test. | Model uptake, classifier failure, or exploitability. | Evaluate the full moderation and inference pipeline. |
Shows how bytes, graphemes, pretokens, synthetic subwords, truncation, and overlap can disagree.
Expected boundary: All synthetic outputs remain deterministic and clearly labelled as non-vendor data.
Open prepared laboratoryNo single view is automatically authoritative. Preserve the source, identify each parser or receiver, and compare their outputs before authorizing a consequential decision.
The exact UTF-8 input before text processing.
Code-point and optional normalization copies.
Whitespace, punctuation, casing, and regex boundaries.
Educational BPE, WordPiece, and Unigram-style pieces.
Synthetic IDs and fallback markers from the bundled vocabularies.
Special tokens, truncation, chunks, and overlap.
The workflow preserves evidence before transformation and keeps structural inspection separate from execution, remote verification, or model behavior.
These controls are contextual. They reduce a defined risk; they do not guarantee safety, truth, attribution, or resistance to every adaptive attack.
Never assume one tokenizer represents another model or version.
Moderate the exact canonical representation and model-bound input, not only display text.
Escape or isolate user text from special-token and chat-template construction.
Chunk at model token boundaries while preserving source offsets.
Include tokenizer files and configuration in software and model provenance.
Definitions are linked into the site-wide glossary and back to the full report.
Rule-based segmentation before a statistical subword algorithm.
A token unit smaller than a word and often larger than a character.
A vocabulary-building method that repeatedly merges frequent adjacent units.
A subword method commonly using likelihood-oriented vocabulary selection and continuation markers.
A probabilistic segmentation model that selects among vocabulary pieces by score.
Encoding unknown text as tokens representing its UTF-8 bytes.
A reserved token representing structure or control rather than ordinary user text.
Discarding input tokens beyond a bounded context limit.
Reconstructing text from token IDs, potentially after lossy preprocessing.
These links are provided for visitors who want the governing specification, paper, framework, or implementation documentation. Links open in a new tab; the site does not fetch them during runtime analysis.
Language-independent subword tokenization and detokenization.
Original research or formal conference publication.Foundational BPE application to open-vocabulary translation.
Original research or formal conference publication.Grapheme, word, and sentence boundaries.
Normative or first-party specification material.Canonical and compatibility normalization.
Normative or first-party specification material.Practical algorithm comparisons and implementation concepts.
Tool, vendor, or implementation documentation; behavior is version-specific.Versioned byte-pair tokenizer implementation.
Tool, vendor, or implementation documentation; behavior is version-specific.The full submitted report is preserved byte-for-byte in the governed research library and in durable repository documentation. The laboratory turns selected concepts into deterministic local output without external calls or hidden persistence.
A detailed account of byte, Unicode, normalization, pre-tokenization, BPE, WordPiece, Unigram, SentencePiece, fallback, special-token, truncation, chunking, and model/tokenizer mismatch behavior.
Read governed reportCompare deterministic miniature character, byte, BPE, WordPiece, and Unigram-style profiles, then inspect local round trips and bounded chunks. The bundled vocabularies do not reproduce a vendor tokenizer.
Open bounded laboratory