Icelandic Language
Ratings reflect this model as run on Arli AI.
Gemma-4-31B-Adetayo is a text generation model available on Arli AI. It offers 31B parameters, 262K token context, reasoning support, vision input. Recommended use: Icelandic Language. Run it through the OpenAI-compatible chat completions API with unlimited requests and zero-log privacy.
View on Hugging FaceBack to ModelsInput (24H)
96
Output (24H)
682
Input (7D)
96
Output (7D)
682
Typical Total Time
30.47s
Typical TTFT
7.48s
Typical Input TPS
1661.81
Typical Output TPS
14.72
Context Size
262144
Quantization
r128 on FP8
Engine
vllm
Creation Method
Finetune
Model Class
Gemma-4-31B - LoRA
Chat Template
Gemma4
Reasoning
Yes
Vision
Yes
Parameters
31B
Added At
10/31/2026
Gemma-4-31B-Adetayo is available through an OpenAI-compatible chat completions endpoint. Point any OpenAI SDK at https://api.arliai.com/v1, pass Gemma-4-31B-Adetayo as the model, and requests are served without logging.
cURL
curl https://api.arliai.com/v1/chat/completions \
-H "Authorization: Bearer $ARLIAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "Gemma-4-31B-Adetayo",
"messages": [{"role": "user", "content": "Hello!"}]
}'Python
from openai import OpenAI
client = OpenAI(
base_url="https://api.arliai.com/v1",
api_key="YOUR_ARLIAI_API_KEY",
)
response = client.chat.completions.create(
model="Gemma-4-31B-Adetayo",
messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)See the text generation docs for every parameter, or read the quick start guide to make your first request.
license: gemma base_model: google/gemma-4-31b-it base_model_relation: finetune language:
Icelandic fine-tune of Google Gemma 4 31B instruction-tuned, targeting Icelandic morphology and grammar.
| 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.
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.
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.