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---
inference: false
library_name: transformers
base_model: google/gemma-3-12b-it
language:
- uk
datasets:
- Goader/kobza
- QIRIM/crh_monocorpus
multilinguality:
- multililingual
tags:
- gemma-3-tokenizer
- ukraine
- corpus-linguistics
pretty_name: “gemma-3 - ukrainized gemma tokenizer”
---
### Using the same approach as [Tereshchenko Blue](https://huggingface.co/transhumanist-already-exists/tereshchenkoblue-tokenizer), now trained on the full [Kobza corpus](https://huggingface.co/datasets/Goader/kobza).
By adding more than 80K Ukrainian tokens **without removing any English or EU languages tokens**, Lapa Tokenizer makes Ukrainian the core language in the multilingual Gemma-3 tokenizer while keeping the vocabulary fixed at its original size of 256K tokens.
### How to possible
More than 16 of the most popular writing systems in the world were analyzed.
Roughly four-fifths of tokens in scripts geographically and culturally distant from Ukraine—for example Bengali, Thai, Chinese, Japanese, and Korean—were pruned.
### Replaced tokens
|Writing system|Tokens removed|Tokens retained|
|-|-|-|
|Han (Chinese)|16,488|4,122|
|Devanagari (Hindi)|10,976|2,743|
|Bengali|7,983|1,995|
|Arabic|6,730|1,682|
|Hiragana / Katakana (Japanese)|3,944|985|
|Hangul (Korean)|3,744|935|
|Tamil|3,080|770|
|Thai|1,740|435|
|Malayalam|1,566|391|
|Telugu|1,428|356|
|Gujarati|1,080|270|
|Kannada|1,016|253|
|Ethiopic|691|172|
|Hebrew|670|167|
|Khmer|481|119|
|Sinhala|435|108|
|Myanmar|410|102|
|Lao|243|60|
|Gurmukhi|215|53|
|Tibetan|107|26|
|Oriya|100|25|
|Cyrillic|13,398|0|
|Gemma-3 \<unused-*\>|6,139|102|
## Feature Overview:
1. +81,492 new Cyrillic BPE tokens trained on the full [Kobza corpus](https://huggingface.co/datasets/Goader/kobza) plus the Cyrillic slice of the [Crimean Tatar corpus](https://huggingface.co/datasets/QIRIM/crh_monocorpus).
2. Just tokens from `Replaced tokens` table was replaced, no any tokens from other Writing system was affected.
3. Unchanged tokens preserve their IDs, enabling direct reuse of Gemma-3 embeddings.
4. Vocab size, Special-token set, pre/post-tokenisation logic, and output formatting match Gemma-3 one-for-one.
5. Reasoning tokens <think></think>
## Simple example
```python
tokenizer = AutoTokenizer.from_pretrained("lapa-llm/tokenizer")
toks = tokenizer("Всі красиві зберігають оптимізм", add_special_tokens=False)
print(len(toks.input_ids)) -only 4 tokens 💪🏻
```
### "fixed" - means that we remove condition that allow to add empty `<think></think>` for hybrid approach. This significantly speeds up tokenization. |