kitoken
Tokenizer for language models.
Tokenize text for Llama, Gemini, GPT-5, DeepSeek, Mistral and many others; in the web, on the client and any platform.
from kitoken import Kitoken
encoder = Kitoken.from_web("hf:Qwen/Qwen3.5-9B")
tokens = encoder.encode("hello world!", True)
string = encoder.decode(tokens).decode("utf-8")
assert string == "hello world!"
Overview
Kitoken is a fast and versatile tokenizer for language models compatible with SentencePiece, HuggingFace Tokenizers, OpenAI Tiktoken and Mistral Tekken, supporting BPE, Unigram and WordPiece tokenization.
- Fast and efficient tokenization
Faster than most other tokenizers in both common and uncommon scenarios; see the benchmarks for comparisons with different datasets. - Runs in all environments
Native in Rust and with bindings for Web, Node and Python; see kitoken.dev for a web demo. - Supports input and output processing
Including unicode-aware normalization, pre-tokenization and post-processing options. - Compact data encoding
Definitions are stored in an efficient binary format and without merge list.
See the main README for more information.
Metadata
Release files for kitoken 0.11.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| kitoken-0.11.1.tar.gz | 69.7 kB | Without provenance | Details for kitoken-0.11.1.tar.gz |
Built distributions (wheels)
Total release size: 29.5 MB
Release files / kitoken-0.11.1.tar.gz
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