gigatoken 0.4.0

Tokenize your documents at GB/s

Gigatoken

400-1600x faster than HuggingFace's tokenizers, drop-in replacement.

Tokenize your text data at GB/s!

Keep in mind that both HF tokenizers and tiktoken are already running multithreaded Rust!

What is Gigatoken?

Gigatoken is the fastest tokenizer for language modeling. It supports a wide range of CPU hardware, and nearly all commonly used tokenizers.

Installation

pip install gigatoken

Usage

Gigatoken can be used with its own API, or in compatibility mode with HuggingFace Tokenizers or Tiktoken.

Compatibility Mode (Easiest)

import gigatoken as gt

# Minimum change from existing HuggingFace tokenizers usage (compatibility mode)
hf_tokenizer = ...
tokenizer = gt.Tokenizer(hf_tokenizer).as_hf()

# tokenizer can be used in the same contexts as hf_tokenizer
tokens = tokenizer.encode_batch(["This is a tes runningt string", "And here is another"])

A substantial amount of effort has been put into making sure the outputs match exactly with what you would get with HuggingFace Tokenizers in this setting, but this is at a non-negligible cost to performance. You can still expect way faster performance across the board, but not quite the 1000x you will get with the Gigatoken API.

Gigatoken API (Fastest)

import gigatoken as gt

tokenizer = gt.Tokenizer("Qwen/Qwen3-8B")  # Accepts HF model names
file_source = gt.TextFileSource(["owt_train.txt"], separator=b"<|endoftext|>")
tokens = tokenizer.encode_files(file_source)

Using the Gigatoken API lets the Rust implementation read data directly, and skips as much overhead as possible while allowing for maximum parallelism. Keep in mind that passing Python data structures through this API still incurs the overhead of reading from Python.

AI Use Disclosure

A majority of this code base was crafted by hand without any use of AI (which can be seen from the project's Git history). In the final stages of the project, AI was used to assist:

• Implementing the user-facing API
• Widening of compatibility, for instance generalizing and porting the pretokenizer implementations to support more tokenizers, less interesting features like padding/truncation/unicode normalization
• Porting SIMD strategies between AVX512/AVX2/NEON
• Final profiling stages and the last ~4x worth of performance from eliminating branch prediction and improving the pretoken cache hierarchy
• Refactoring and code reuse