tokie is a Rust tokenizer library (with Python bindings) that can load any tokenizer on HuggingFace and tokenize 15-29x faster. It supports every major algorithm — BPE, WordPiece, SentencePiece, and Unigram — and is 100% token-accurate, every time.

pip install tokie

[dependencies]
tokie = { version = "0.0.8", features = ["hf"] }

import tokie

# Load any HuggingFace tokenizer
tokenizer = tokie.Tokenizer.from_pretrained("bert-base-uncased")

# Encode — returns Encoding with ids, attention_mask, type_ids, tokens
encoding = tokenizer("Hello, world!")  # or tokenizer.encode("Hello, world!")
print(encoding.ids)             # [101, 7592, 1010, 2088, 999, 102]
print(encoding.tokens)          # ['[CLS]', 'hello', ',', 'world', '!', '[SEP]']
print(encoding.attention_mask)  # [1, 1, 1, 1, 1, 1]

# Decode
text = tokenizer.decode(encoding.ids)  # "hello , world !"

# Count tokens without allocating
count = tokenizer.count_tokens("Hello, world!")  # 6

# Batch encode (parallel across all cores)
encodings = tokenizer.encode_batch(["Hello!", "World"], add_special_tokens=True)

use tokie::Tokenizer;

let tokenizer = Tokenizer::from_pretrained("bert-base-uncased")?;
let encoding = tokenizer.encode("Hello, world!", true);
println!("{:?}", encoding.ids);             // [101, 7592, 1010, 2088, 999, 102]
println!("{:?}", encoding.attention_mask);  // [1, 1, 1, 1, 1, 1]

let text = tokenizer.decode(&encoding.ids).unwrap();

For ML inference, you need fixed-length inputs. tokie supports padding and truncation just like HuggingFace:

tokenizer = tokie.Tokenizer.from_pretrained("bert-base-uncased")

# Truncate to max length
tokenizer.enable_truncation(max_length=128)

# Pad to fixed length (or use BatchLongest for dynamic padding)
tokenizer.enable_padding(length=128, pad_id=0)

# All outputs are now exactly 128 tokens
results = tokenizer.encode_batch(["Short text", "A much longer piece of text for testing"])
assert all(len(r) == 128 for r in results)

# attention_mask shows which tokens are real (1) vs padding (0)
print(results[0].attention_mask)  # [1, 1, 1, 1, 0, 0, 0, ...]

For rerankers and cross-encoders that need sentence pairs with token type IDs:

pair = tokenizer("How are you?", "I am fine.")  # or tokenizer.encode_pair(...)
pair.ids               # [101, 2129, 2024, 2017, 1029, 102, 1045, 2572, 2986, 1012, 102]
pair.attention_mask    # [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
pair.type_ids          # [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
pair.special_tokens_mask  # [1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]

Track where each token maps back to in the original text:

enc = tokenizer.encode_with_offsets("Hello world")
for token_id, (start, end) in zip(enc.ids, enc.offsets):
    print(f"  token {token_id}: bytes [{start}:{end}]")

tokenizer.vocab_size          # 30522
tokenizer.id_to_token(101)    # "[CLS]"
tokenizer.token_to_id("[SEP]")  # 102
vocab = tokenizer.get_vocab()   # {"[CLS]": 101, "[SEP]": 102, ...}

tokie's binary format is ~10x smaller than tokenizer.json and loads in ~5ms:

tokenizer.save("model.tkz")
tokenizer = tokie.Tokenizer.from_file("model.tkz")

from_pretrained() automatically tries .tkz first, falling back to tokenizer.json.

All benchmarks run on 1 MB of enwik8 on an Apple M3 Pro. tokie produces identical output to HuggingFace tokenizers — every token matches, every time.

For tiktoken-style BPE models, tokie uses a backtracking encoder built on an Aho-Corasick automaton. Instead of iteratively merging byte pairs, it does a greedy longest-match in O(n) time, with backtracking only when adjacent tokens form invalid pairs. Combined with parallel chunking across all cores and hand-coded pretokenizers from pretokie, this gives 15-24x faster than HuggingFace.

WordPiece tokenizers use a different algorithm — greedy longest-match prefix search over a vocabulary trie. tokie uses a pre-built Double-Array trie for O(n) lookup with excellent cache locality, combined with a specialized BERT pretokenizer. The result is 20-29x faster than HuggingFace on BERT, with identical output.

SentencePiece-style models use a different merge algorithm with non-topological rank orders. tokie uses a radix heap with O(1) amortized operations that exploits BPE's monotonic rank property. tokie is 2-3x faster than HuggingFace on Gemma 3.

All results on Apple M3 Pro, single-string encode, median of 10 runs.

100% token-accurate across all models. Batch encoding is 6-9x faster than HF.

Loading a tokenizer from tokenizer.json requires JSON parsing, vocabulary construction, and — for BPE models — building the Aho-Corasick automaton from scratch. tiktoken similarly has to parse its BPE data and compile regex patterns on every load. tokie's .tkz binary format stores all of this pre-built: the Double-Array Aho-Corasick (DAAC) automaton state, the normalized vocabulary, and the encoder configuration are serialized directly. Loading becomes a near-zero-cost deserialization — no parsing, no construction — achieving 2x–8x faster cold load times than HuggingFace.

Every tokenizer below is tested against the original HuggingFace tokenizer on 1MB of enwik8 (~300K tokens) in CI. Pass = every token matches.

Summary: 74 pass, 0 fail out of 74 tested. Every tokenizer produces identical output to HuggingFace.

When I started building Chonkie, the biggest bottleneck wasn't chunking — it was tokenization. We were spending more time counting tokens than actually chunking text.

tokie uses hand-written parsers for each pretokenization pattern — GPT-2, cl100k, o200k, BERT — that understand the exact character classes needed without the overhead of a general-purpose regex engine. That alone gets you a 3.5x speedup on pretokenization.

The second problem was that no single library could load everything. I actually tried to solve this before with AutoTikTokenizer, believing tiktoken's BPE engine could handle all of HuggingFace. I was wrong — you need fundamentally different algorithms for each encoder type: backtracking BPE for tiktoken-style models, heap-based BPE for models with non-topological merge orders, radix-heap BPE for SentencePiece, plus WordPiece and Unigram each with their own tricks.

The third insight was parallelism. Tokenization is embarrassingly parallel if you split text at the right boundaries. We use chunk to SIMD-split text into chunks that respect token boundaries, then encode each chunk on a separate core and concatenate. This gives near-linear scaling — about 5x on 8 cores.

Finally, we built the .tkz format to eliminate load-time overhead. A tokenizer.json file has to be parsed, validated, and used to reconstruct all the internal data structures (including the Aho-Corasick automaton, which is expensive to build for large vocabularies). The .tkz format stores the pre-built DAAC automaton, vocabulary, and configuration as a flat binary — loading is just deserialization, no construction required. This cuts load times from 283ms to 118ms for large models like O200K, and from 101ms to 12ms for BERT.

The result is tokie — one tokenizer to rule them all.

tokie builds on ideas from HuggingFace tokenizers, tiktoken, GitHub's rust-gems (backtracking BPE via Aho-Corasick), and chunk (SIMD text splitting).

If you use tokie in your research, please cite it as follows:

@software{tokie2025,
  author = {Minhas, Bhavnick},
  title = {tokie: Fast, correct tokenizer library for every HuggingFace model},
  year = {2025},
  publisher = {GitHub},
  howpublished = {\url{https://github.com/chonkie-inc/tokie}},
}