xerv-crayon 2.0.3

Production-grade tokenizer achieving >16M tokens/s via AVX2/SIMD optimizations and Double-Array Trie engine.

🖍️ XERV Crayon

The Cartridge-Based Tokenizer for Specialized AI

Why force a single bloated vocabulary on every problem?
Crayon is a next-generation tokenizer designed for specialization. Hot-swap vocabulary profiles ("Cartridges") optimized for your domain—Quantum Physics, Rust Programming, Financial Law, or anything in between.

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🚀 Key Features

Feature	Description
💾 Cartridge System	Instantly hot-swap specialized vocabularies (science, code, multilingual)
⚡ AVX2 Double-Array Trie	Validated ~10M tokens/sec via SIMD-accelerated branchless tokenization
🗺️ Zero-Copy Memory Mapping	DAT files loaded via mmap for instant startup & minimal RAM
🌊 Zero-Disk Streaming	Build profiles directly from Hugging Face—no multi-GB downloads
🛡️ Offline Resilience	Seamless local bootstrap fallback. Works offline out-of-the-box
🧠 Entropy-Guided Construction	Information-theoretic token selection for maximum domain efficiency

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📊 Benchmarks — The Numbers Speak

100% HONEST. NO SUGARCOATING. DATA-DRIVEN.

Run python benchmark_competitive.py to reproduce these results yourself.

⚡ Speed Comparison

Tokenizer	Tokens/sec	vs CRAYON
🖍️ CRAYON (lite, 50k)	6,010,525	baseline
tiktoken (GPT-4)	524,469	11.5x slower
tiktoken (GPT-3)	466,823	12.9x slower
HF LLaMA (SP-BPE)	281,558	21.3x slower
HF GPT-2 (BPE)	237,117	25.3x slower
HF BERT (WordPiece)	202,269	29.7x slower
HF T5 (SentencePiece)	189,928	31.6x slower

📈 Full Benchmark Results

Tokenizer	Vocab Size	Tokens/sec	MB/sec	Load Time	Avg Time
CRAYON (lite, 50k)	50,000	6,010,525	15.33	0.54ms	4.56ms
tiktoken (cl100k/GPT-4)	100,000	524,469	2.18	0.01ms	32.03ms
tiktoken (p50k/GPT-3)	50,000	466,823	1.55	0.00ms	44.98ms
HF LLaMA (SP-BPE)	32,000	281,558	0.95	1212.02ms	73.52ms
HF GPT-2 (BPE)	50,257	237,117	0.69	2051.18ms	100.79ms
HF BERT (WordPiece)	30,522	202,269	0.73	1603.10ms	95.43ms
HF T5 (SentencePiece)	32,000	189,928	0.68	1727.91ms	102.15ms

📋 Test Environment & Methodology

• Platform: Windows AMD64, Python 3.13.1
• Test Text: 68.4 KB mixed content (code, prose, multilingual)
• Iterations: 10 runs + 2 warmup per tokenizer
• Full methodology: BENCHMARK_RESULTS.md

🏆 Key Takeaways

Metric	Result
✅ vs tiktoken (GPT-4)	11.5x faster
✅ vs HuggingFace GPT-2	25x faster
✅ Load time	0.54ms (vs 1-2s for HuggingFace)
✅ Peak throughput	10.4M tokens/sec (science profile)

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⚡ Quick Start

Get tokenizing in under 60 seconds:

Option 1: Direct DAT Compilation

import json
import mmap
from crayon.c_ext.dat_builder import DATBuilder
from crayon.c_ext import crayon_fast

# Load any trained vocabulary
with open("trained_vocab_code.json", "r") as f:
    vocab_list = json.load(f)

# Compile to DAT (one-time, few seconds)
builder = DATBuilder()
builder.build(vocab_list)
builder.save("vocab_code.dat")

# Load into C++ engine via memory mapping (instant, <1ms)
with open("vocab_code.dat", "rb") as f:
    mm = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
    crayon_fast.load_dat(mm)

# Ultra-fast tokenization 🚀
code = 'fn main() { println!("Hello, World!"); }'
tokens = crayon_fast.tokenize(code)
print(f"Tokens: {tokens}")

Option 2: Profile System (Recommended)

from crayon.core.vocabulary import CrayonVocab

# Load pre-compiled profile (requires one-time compile_profiles.py)
vocab = CrayonVocab.load_profile("code")
tokens = vocab.tokenize("fn main() { }")
decoded = vocab.decode(tokens)
print(f"Decoded: {decoded}")

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📦 Installation

git clone https://github.com/Xerv-AI/crayon.git
cd crayon
pip install -e .

Build the AVX2 Extension

python setup.py build_ext --inplace

Note: Requires a C++ compiler (MSVC on Windows, GCC/Clang on Linux/Mac).

🔧 One-Time Setup: Compile Profiles

# Builds .dat files → ~/.cache/xerv/crayon/profiles/
python compile_profiles.py

Each profile takes 38ms-26s depending on size. See DAT_BUILDING_EXPLAINED.md for details.

🧪 Verify Installation

python demo_tokenize.py

Expected output:

[1] Loading 'lite' profile...
    Status: 🚀 Fast C++ DAT Engine
[2] Tokenizing: 'Hello, world! This is Crayon.'
    Tokens IDs: [...]

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🏎️ DAT Engine V2 Architecture

Crayon V2 uses a "God Tier" implementation combining:

┌─────────────┐      ┌──────────────┐      ┌─────────────┐      ┌──────────────┐
│ vocab.json  │ ──▶  │ DATBuilder   │ ──▶  │  vocab.dat  │ ──▶  │  C++ Engine  │
│   (List)    │      │  (Python)    │      │  (Binary)   │      │   (AVX2)     │
└─────────────┘      └──────────────┘      └─────────────┘      └──────────────┘

Component	File	Purpose
Offline Compiler	dat_builder.py	First-Fit algorithm → compact DAT binary
AVX2 Runtime	engine.cpp	Branchless state transitions + SIMD parallel ASCII
Zero-Copy Loader	mmap + buffer protocol	Instant startup, minimal RAM

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🧩 Available Cartridges

5 production-ready profiles defined in src/crayon/core/profiles.py:

Profile	Size	Optimized For	Sources
lite	50k	Speed & Mobile	WikiText, RainDrop
science	250k	Reasoning (LaTeX, Quantum, Grad Math)	GRAD, Physics-700
code	250k	Syntax (Python, Rust, C++, JS)	CodeParrot, The Stack
multilingual	250k	Global (EU langs, Chinese, Hindi)	OSCAR, Wikipedia
arts_commerce	250k	Business (Legal, Finance, Lit)	PG19, Fin Phrasebank

vocab = CrayonVocab.load_profile("science")
vocab = CrayonVocab.load_profile("multilingual")

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🛠️ Advanced Usage

Compile Vocabulary to DAT Format

from crayon.c_ext.dat_builder import DATBuilder
import json

with open("trained_vocab_lite.json", "r") as f:
    vocab = json.load(f)

builder = DATBuilder()
builder.build(vocab)
builder.save("vocab_lite.dat")

Direct C++ Engine Access

import mmap
from crayon.c_ext import crayon_fast

with open("vocab_lite.dat", "rb") as f:
    mm = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
    crayon_fast.load_dat(mm)

tokens = crayon_fast.tokenize("Your text here")

Force Rebuild / Offline Mode

# Rebuild from local resources only (fastest)
vocab = CrayonVocab.load_profile("arts_commerce", force_rebuild=True)

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🏗️ Architecture

Layer	File	Purpose
Builder	c_ext/dat_builder.py	Offline DAT compiler
Engine	c_ext/engine.cpp	AVX2 SIMD runtime
Config	core/profiles.py	Cartridge definitions
Resources	resources.py	Streaming, fallbacks, caching

For a deep dive, read the Engineering Treatise.

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🧪 Testing

# All tests
python -m pytest tests/ -v

# DAT engine tests
python -m pytest tests/test_c_ext.py -v

14/14 tests pass: DATBuilder, C++ module, full pipeline, Python fallback.

🔬 DAT Engine Verification

python verify_dat_engine.py

============================================================
XERV CRAYON V2.0 - HYPER-PRODUCTION DAT ENGINE VERIFICATION
============================================================
Vocabulary Size: 50,000 tokens
DAT Nodes: 163,000+
Throughput: 9,786,707 tokens/sec
STATUS: ✅ HYPER-PRODUCTION READY

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📊 Training Data

Dataset	Size	Samples	Domain
Tiny Shakespeare	1.06 MB	1 (Full)	Classical Literature
RainDrop-DTS	179 KB	3,210	Instruction Following
Physics	332 KB	700	Scientific Reasoning
GRAD Math	5.00 MB	500*	Graduate Mathematics
TOTAL	~6.56 MB	4,411	Curated Corpus

_{*GRAD dataset limited to 500 high-density samples for efficient default build.}

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🧩 API Reference

CrayonVocab

# Constructors
CrayonVocab(tokens: List[str], unk_token: str = "<UNK>")
CrayonVocab.from_corpus(corpus: str, target_size: int = 500000)
CrayonVocab.from_default_sources(vocab_size: int = 500000)
CrayonVocab.from_file(path: str)
CrayonVocab.from_json(path: str)
CrayonVocab.load_profile(name: str)  # Load cached DAT profiles

# Methods
vocab.tokenize(text: str) -> List[int]
vocab.decode(token_ids: List[int]) -> str
vocab.save(path: str, format: str = "txt")

DAT Builder

from crayon.c_ext.dat_builder import DATBuilder

builder = DATBuilder()
builder.build(vocab_list: List[str])
builder.save(output_path: str)

C++ Engine

from crayon.c_ext import crayon_fast

crayon_fast.load_dat(buffer)  # bytes, mmap, or memoryview
crayon_fast.tokenize(text: str) -> List[int]

Utilities

from crayon import check_c_extension, check_resources

print(check_c_extension())  # True/False
print(check_resources())     # Available data sources

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🤝 Contributing

We welcome contributions! Whether it's new cartridges, performance optimizations, or bug fixes—open an issue or submit a PR.

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📜 Citation

@techreport{xerv2026crayon,
  title={XERV Crayon: A First-Principles Analysis of Production-Grade Tokenization},
  author={Pal, Soham and Xerv Research},
  year={2026},
  institution={Xerv Research Engineering Division}
}

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📄 License

Copyright (c) 2025-2026 Xerv Research. Released under the MIT License.

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Built with 💙 by Xerv Research Engineering Division

_{⭐ Star this repo if Crayon helps your project!}