Instructions to use Goader/gemma-3-4b-it-uk-matt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Goader/gemma-3-4b-it-uk-matt with Transformers:

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

pipe = pipeline("text-generation", model="Goader/gemma-3-4b-it-uk-matt")
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("Goader/gemma-3-4b-it-uk-matt")
model = AutoModelForMultimodalLM.from_pretrained("Goader/gemma-3-4b-it-uk-matt", 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 Goader/gemma-3-4b-it-uk-matt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Goader/gemma-3-4b-it-uk-matt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Goader/gemma-3-4b-it-uk-matt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Goader/gemma-3-4b-it-uk-matt

SGLang

How to use Goader/gemma-3-4b-it-uk-matt 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 "Goader/gemma-3-4b-it-uk-matt" \
    --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": "Goader/gemma-3-4b-it-uk-matt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Goader/gemma-3-4b-it-uk-matt" \
        --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": "Goader/gemma-3-4b-it-uk-matt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Goader/gemma-3-4b-it-uk-matt with Docker Model Runner:
```
docker model run hf.co/Goader/gemma-3-4b-it-uk-matt
```

gemma-3-4b-it-uk-matt

google/gemma-3-4b-it transferred to the Ukrainian-centric Lapa tokenizer with MATT (Model-Aware Tokenizer Transfer). The tokenizer keeps the original Gemma vocabulary and adds Ukrainian tokens, cutting the token count on Ukrainian text by roughly a third. Only the input embeddings differ from the original model; the chat template, special tokens and generation config are unchanged.

Base-model counterpart: Goader/gemma-3-4b-uk-matt.

Recipe

Embeddings initialized with FOCUS from gemma-3-4b-it, with every token present in the original vocabulary copied exactly from the original model, then trained with the AIM objective against the frozen original model: layers 1–16 of 34, all_words_last_token alignment, MSE loss, lr 1e-4, original-vocabulary embeddings frozen.
One pass over 1.03M Ukrainian documents (Kobza corpus, 256-token windows), ~6 h on one H100.
Weights stored in bfloat16 with the full 262,208-row embedding table.

Results (Ukrainian, 3-shot, greedy, base-model prompts without chat template)

metric	gemma-3-4b-it (original tokenizer)	FOCUS init	this model
Belebele uk (acc)	77.7	38.0	64.0
Global MMLU uk (acc)	49.8	31.4	41.5
FLORES en→uk (BLEU / chrF)	26.3 / 57.5	24.3 / 55.2	25.4 / 56.8
FLORES uk→en (BLEU / chrF)	39.6 / 66.9	15.1 / 42.0	33.7 / 60.8
WMT en→uk (BLEU)	7.2	6.1	9.4
XLSum uk (ROUGE-1)	6.9	5.5	5.6

Generation into Ukrainian is essentially fully recovered (97–99 % on FLORES en→uk). Understanding-heavy tasks recover ~82–91 %, less than the base-model transfer (94–98 %), so this model trades some accuracy on Ukrainian comprehension for ~35 % shorter Ukrainian sequences.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("Goader/gemma-3-4b-it-uk-matt")
model = AutoModelForCausalLM.from_pretrained("Goader/gemma-3-4b-it-uk-matt", torch_dtype="bfloat16", device_map="auto")

messages = [{"role": "user", "content": "Поясни простими словами, що таке фотосинтез."}]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=256)
print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))

Text-only: the vision tower is inherited unchanged but the image path was not evaluated.

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for Goader/gemma-3-4b-it-uk-matt

Base model

google/gemma-3-4b-pt

Finetuned

google/gemma-3-4b-it

Finetuned

(779)

this model