nanotok 0.1.0

High-performance BPE tokenizer. 20-60x faster than tiktoken.

nanotok

A high-performance BPE tokenizer written in C++ with Python bindings. 20-60x faster than tiktoken.

Installation

pip install nanotok

With optional dependencies:

pip install "nanotok[all]"  # includes huggingface-hub and jinja2

Quick Start

from nanotok import Tokenizer

# Load from Hugging Face Hub (requires huggingface-hub)
tokenizer = Tokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")

# Load from tiktoken encoding
tokenizer = Tokenizer.from_tiktoken("cl100k_base")

# Load from local file
tokenizer = Tokenizer.from_file("path/to/tokenizer.json")

# Encode/decode
ids = tokenizer.encode("Hello, world!")
text = tokenizer.decode(ids)

# Batch processing
batch_ids = tokenizer.encode_batch(["Hello", "World"])
batch_texts = tokenizer.decode_batch(batch_ids)

# HuggingFace-style API
result = tokenizer("Hello, world!", padding=True, return_tensors="pt")
print(result["input_ids"], result["attention_mask"])

# Chat templates (requires jinja2)
messages = [{"role": "user", "content": "Hello!"}]
rendered = tokenizer.apply_chat_template(messages, tokenize=False)

Features

• Fast: 20-60x faster than tiktoken, written in C++ with SIMD optimizations
• Compatible: Drop-in replacement for tiktoken and HuggingFace tokenizers
• Batch processing: Efficient batch encode/decode
• Chat templates: Support for Jinja2 chat templates
• Special tokens: Full support for special token handling
• Cache: Built-in encoding cache for repeated text

API Reference

Tokenizer

Class Methods

• from_file(path) - Load from tokenizer.json file
• from_pretrained(repo_id) - Load from Hugging Face Hub
• from_tiktoken(encoding_name) - Load from tiktoken encoding (gpt2, r50k_base, p50k_base, cl100k_base, o200k_base)

Methods

• encode(text, allowed_special=None, add_special_tokens=False) - Encode text to token IDs
• decode(ids, skip_special_tokens=False) - Decode token IDs to text
• encode_batch(texts, ...) - Batch encode
• decode_batch(batch_ids, ...) - Batch decode
• token_to_id(token) - Get ID for token
• id_to_token(id) - Get token for ID
• apply_chat_template(messages, tokenize=True, add_generation_prompt=False) - Apply chat template
• clear_cache() - Clear encoding cache
• set_cache_enabled(enabled) - Enable/disable cache

Properties

• vocab_size - Vocabulary size
• special_tokens - Dict of special tokens
• eos_token, bos_token, pad_token, unk_token - Special token strings
• eos_token_id, bos_token_id, pad_token_id, unk_token_id - Special token IDs

Development

# Clone and install with uv
git clone https://github.com/ishaan/nanotok
cd nanotok
uv sync

# Run tests
uv run pytest

# Build wheel
uv build

License

MIT