Instructions to use lapa-llm/tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lapa-llm/tokenizer with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="lapa-llm/tokenizer")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("lapa-llm/tokenizer")
model = AutoModelForMultimodalLM.from_pretrained("lapa-llm/tokenizer", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lapa-llm/tokenizer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lapa-llm/tokenizer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lapa-llm/tokenizer",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/lapa-llm/tokenizer

SGLang

How to use lapa-llm/tokenizer with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "lapa-llm/tokenizer" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lapa-llm/tokenizer",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "lapa-llm/tokenizer" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lapa-llm/tokenizer",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use lapa-llm/tokenizer with Docker Model Runner:
```
docker model run hf.co/lapa-llm/tokenizer
```

tokenizer

File size: 2,627 Bytes

---
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.