File size: 5,218 Bytes
---
language:
  - en
  - de
  - fr
  - es
  - pt
  - it
  - nl
  - pl
  - ro
  - cs
  - sv
  - da
  - "no"
  - fi
  - hu
  - hr
  - bg
  - tr
  - ca
  - ru
  - uk
  - sr
  - zh
  - ja
  - ko
  - ar
  - fa
  - he
  - hi
  - bn
  - th
  - vi
  - ka
  - hy
  - el
  - yi
  - ur
  - ta
  - te
  - gu
  - pa
  - ml
  - kn
  - am
  - si
  - my
  - km
  - mr
  - ne
  - or
  - bo
  - dv
  - eu
  - gl
  - gd
  - et
  - sk
  - lt
  - sl
  - lv
  - af
  - sq
  - sw
  - is
  - tl
  - cy
  - ga
  - br
  - la
  - mk
  - id
  - code
license: apache-2.0
library_name: tokenizers
tags:
  - tokenizer
  - bpe
  - multilingual
  - code
  - quartz
  - aenea
  - coding
  - python
  - flores
pipeline_tag: text-generation
---

# QT_V.2 Code 114K — Multilingual Coding Tokenizer

**Lowest total tokens on our 66-test field benchmark of any tokenizer at any vocab size.** 114,688 vocabulary optimised for multilingual coding models. Trained with doubled code weight (37% of corpus) including 450K high-quality Python functions from CodeSearchNet. Beats Llama 3, Tekken, and Qwen 2.5 on total tokens while using 10–37% less vocabulary. Validated on FLORES-200 across 204 languages.

Part of the **QT_V.2 tokenizer family** by [Quartz Data Infrastructure](https://quartz.host), the open data layer behind [AENEA](https://aenea.app).

## FLORES-200 Results (204 Languages · 1,012 Parallel Sentences)

| Metric | QT Code 114K | QT 96K | QT 64K | Llama 3 (128K) | Tekken (131K) | Qwen 2.5 (152K) |
|---|---|---|---|---|---|---|
| **Total tokens** | 13,007,924 | **12,961,617** | 13,592,357 | 16,764,198 | 14,421,539 | 15,425,680 |
| **Equity ratio** | 43.3× | **31.6×** | 41.0× | 118.6× | 127.9× | 77.7× |
| Mean fertility | 4.03 | **3.94** | 4.18 | 5.72 | 5.34 | 4.91 |

QT Code 114K uses **22.4% fewer tokens than Llama 3** and **9.8% fewer than Tekken** across all 204 FLORES languages — with 10–37% less vocabulary.

### Key FLORES Languages (tok/word)

| Language | QT Code | Llama 3 | Tekken | Qwen 2.5 |
|---|---|---|---|---|
| Japanese | **32.1** | 38.9 | 41.3 | 35.8 |
| Tibetan | **46.5** | 149.8 | 168.4 | 98.0 |
| Sinhala | **3.58** | 11.37 | 16.60 | 9.17 |
| Amharic | **3.40** | 11.95 | 11.98 | 6.45 |
| Georgian | **3.46** | 15.47 | 3.93 | 8.33 |
| Odia | **4.10** | 16.90 | 18.30 | 13.65 |

## Field Benchmark (66 Tests)

| Metric | Value |
|---|---|
| **Total tokens** | **3,314** (lowest of any tokenizer) |
| vs Llama 3 (128K) | 41.2% fewer tokens |
| vs Tekken (131K) | 23.8% fewer tokens |
| vs Qwen 2.5 (152K) | 36.1% fewer tokens |

### Code Performance

| Language | QT Code | QT 96K | QT 64K | Llama 3 | Tekken | Qwen 2.5 |
|---|---|---|---|---|---|---|
| Python | **110** | 115 | 125 | 97 | 112 | 105 |
| JavaScript | **67** | 71 | 71 | 65 | 69 | 64 |
| Rust | **111** | 113 | 117 | 108 | 111 | 107 |

Python compression improved from 125 (64K) to 115 (96K) to **110** (Code 114K) — closing the gap versus Llama 3's 97 from 28.9% to 13.4%.

### Category Totals (lower is better)

| Category | QT Code | Llama 3 | Tekken | Qwen 2.5 |
|---|---|---|---|---|
| Natural Languages (20) | **1,033** | 1,599 | 1,038 | 1,535 |
| V1 Expansion (14) | **662** | 1,758 | 1,092 | 1,509 |
| V2 New Scripts (3) | **188** | 692 | 740 | 523 |
| Celtic / Brythonic (8) | **312** | 391 | 341 | 384 |
| Code (3) | 288 | **270** | 292 | 276 |
| **TOTAL (66 tests)** | **3,314** | 5,639 | 4,347 | 5,183 |

## When to Use This Variant

**QT_V.2 Code 114K** is designed for multilingual coding assistants and code generation models. It wins Natural Languages outright (1,033 — beating Tekken's 1,038) while offering competitive code compression. Ideal for models that must serve both code and diverse natural language users.

Also available: [QT_V.2 64K](https://huggingface.co/QuartzOpen/QT_V.2_64K) (smallest embedding) · [QT_V.2 96K](https://huggingface.co/QuartzOpen/QT_V.2_96K) (best all-round)

## Usage

```python
from tokenizers import Tokenizer
tok = Tokenizer.from_file("tokenizer.json")
encoded = tok.encode("def fibonacci(n):\n    if n <= 1:\n        return n\n    return fibonacci(n-1) + fibonacci(n-2)")
print(encoded.tokens)
```

## Specifications

| Spec | Value |
|---|---|
| Vocabulary | 114,688 |
| Languages | 71 natural + 15 code (incl. CodeSearchNet) |
| Script families | 26 |
| Pretokenizer | Llama 3 regex |
| Arithmetic | Single-digit splitting |
| Max token length | 15 chars |
| Avg token length | 6.24 chars |
| Compression | 3.60 chars/token |

## Training

Byte-level BPE with Llama 3 regex pretokenizer. Code-heavy corpus:

| Category | Share | Sources |
|---|---|---|
| Wikipedia | 37.3% | 71 languages (wiki_ultra_clean v7.3) |
| Code | 37.4% | 14 languages + CodeSearchNet Python (450K functions) |
| Stack Exchange | 25.3% | 49 sites (se_ultra_clean v1) |

## Files

`tokenizer.json` · `vocab.json` · `merges.txt` · `training_report.json`

## Contact

Open-source: quartzopensource@gmail.com
Commercial licensing & enterprise: commercial@aeneaglobal.com

## License

Apache 2.0 — Copyright 2025-2026 AENEA Global Ltd

```bibtex
@misc{qt_v2_2026,
  title={QT_V.2: A Multilingual BPE Tokenizer Family},
  author={AENEA Global Ltd},
  year={2026},
  url={https://quartz.host},
}
```