tokenizer

Using the same approach as Tereshchenko Blue, now trained on the full Kobza corpus.

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

Feature Overview:

1. +81,492 new Cyrillic BPE tokens trained on the full Kobza corpus plus the Cyrillic slice of the Crimean Tatar corpus.
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

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.