tokie 0.0.4

Blazingly fast tokenizer — 50x faster, 10x smaller, 100% accurate

50x faster tokenization, 10x smaller model files, 100% accurate

GitHub · crates.io · HuggingFace

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tokie is a fast, correct tokenizer library built in Rust with Python bindings. Drop-in replacement for HuggingFace tokenizers — supports BPE (GPT-2, tiktoken, SentencePiece), WordPiece (BERT), and Unigram encoders.

Installation

pip install tokie

Quick Start

import tokie

# Load from HuggingFace Hub (tries .tkz first, falls back to tokenizer.json)
tokenizer = tokie.Tokenizer.from_pretrained("bert-base-uncased")

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

# Decode
text = tokenizer.decode([101, 7592, 1010, 2088, 999, 102])

# Token count (fast, no Encoding overhead)
count = tokenizer.count_tokens("Hello, world!")

# Vocabulary size
print(tokenizer.vocab_size)  # 30522

Padding & Truncation

# Truncate to max length (special tokens preserved)
tokenizer.enable_truncation(max_length=32)

# Pad all sequences in a batch to the same length
tokenizer.enable_padding(length=32, pad_id=tokenizer.pad_token_id or 0)

# Batch encode — all sequences same length, ready for model input
texts = ["Hello world", "Short", "A much longer sentence for testing"]
batch = tokenizer.encode_batch(texts, add_special_tokens=True)
for enc in batch:
    print(len(enc), enc.ids[:5])  # All length 32

Pair Encoding (Cross-Encoders)

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

Save & Load (.tkz format)

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

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

Supported Models

Works with any HuggingFace tokenizer — GPT-2, BERT, Llama 3/4, Mistral, Phi, Qwen, T5, XLM-RoBERTa, and more.

License

MIT OR Apache-2.0