Instructions to use cyboghostginx/gemma-4-31B-it-Adetayo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cyboghostginx/gemma-4-31B-it-Adetayo with Transformers:

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

pipe = pipeline("text-generation", model="cyboghostginx/gemma-4-31B-it-Adetayo")
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("cyboghostginx/gemma-4-31B-it-Adetayo")
model = AutoModelForMultimodalLM.from_pretrained("cyboghostginx/gemma-4-31B-it-Adetayo", 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 cyboghostginx/gemma-4-31B-it-Adetayo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cyboghostginx/gemma-4-31B-it-Adetayo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cyboghostginx/gemma-4-31B-it-Adetayo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cyboghostginx/gemma-4-31B-it-Adetayo

SGLang

How to use cyboghostginx/gemma-4-31B-it-Adetayo 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 "cyboghostginx/gemma-4-31B-it-Adetayo" \
    --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": "cyboghostginx/gemma-4-31B-it-Adetayo",
		"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 "cyboghostginx/gemma-4-31B-it-Adetayo" \
        --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": "cyboghostginx/gemma-4-31B-it-Adetayo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cyboghostginx/gemma-4-31B-it-Adetayo with Docker Model Runner:
```
docker model run hf.co/cyboghostginx/gemma-4-31B-it-Adetayo
```

gemma-4-31B-it-Adetayo

Icelandic fine-tune of Google Gemma 4 31B instruction-tuned, targeting Icelandic morphology and grammar.

Scores (Miðeind Icelandic LLM leaderboard, official run)

	score
this model, 6-task average	70.09
base gemma-4-31b-it, 6-task average	71.24
this model, 5-task local average (thinking off)	83.59

Read those two top numbers carefully. The base is a reasoning model and its leaderboard run uses its thinking path; this fine-tune is submitted and evaluated with thinking disabled. On the 5-task subset, thinking-on is worth roughly 7 points on this base (85.0 with, 77.92 without), so the fine-tune and the base entry are not measured the same way. Where they are comparable, morphology is the gain.

A reasoning-preserving variant was also trained on rejection-sampled inflection traces. It held Wino, GED, Belebele and ARC exactly but moved inflection not at all, because 293 of 354 traces covered cases the base already solved. The method is sound, the data was too easy, so that variant was not published. This model is at its supervised fine-tuning ceiling.

Use

Standard text generation. Apply the Gemma chat template (tokenizer.apply_chat_template). Load the text path on GPU (Gemma4ForConditionalGeneration, .to("cuda")). Evaluate with thinking disabled.

License

Gemma derivative. Use is governed by the Gemma Terms of Use and the Gemma Prohibited Use Policy. "Gemma" is retained in the model name as required.

Training data and methodology are proprietary and are not distributed with the model.

Downloads last month: 7

Safetensors

Model size

31B params

Tensor type

BF16