Korean Language
Ratings reflect this model as run on Arli AI.
Qwen3.5-27B-QuettaLLMs-Koreasoner-V3-Derestricted-Lite is a text generation model available on Arli AI. It offers 27B parameters, 262K token context, reasoning support, vision input. Recommended use: Korean Language. Run it through the OpenAI-compatible chat completions API with unlimited requests and zero-log privacy.
View on Hugging FaceBack to ModelsInput (24H)
668K
Output (24H)
17K
Input (7D)
3M
Output (7D)
121K
Typical Total Time
6.61s
Typical TTFT
0.97s
Typical Input TPS
960.12
Typical Output TPS
22.82
Context Size
262144
Quantization
r64 on INT8
Engine
vllm
Creation Method
Finetune
Model Class
Qwen3.5-27B-D - LoRA-DRS-Lite
Chat Template
Qwen3.5
Reasoning
Yes
Vision
Yes
Parameters
27B
Added At
5/2/2026
Qwen3.5-27B-QuettaLLMs-Koreasoner-V3-Derestricted-Lite is available through an OpenAI-compatible chat completions endpoint. Point any OpenAI SDK at https://api.arliai.com/v1, pass Qwen3.5-27B-QuettaLLMs-Koreasoner-V3-Derestricted-Lite 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": "Qwen3.5-27B-QuettaLLMs-Koreasoner-V3-Derestricted-Lite",
"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="Qwen3.5-27B-QuettaLLMs-Koreasoner-V3-Derestricted-Lite",
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: apache-2.0 language:
QuettaLLMs-27B-Koreasoner-V3 is a Korean large language model (LLM) fine-tuned using data refined by NewenAI. Built on the Qwen/Qwen3.5-27B base model, it underwent LoRA-based fine-tuning to improve its logical reasoning performance.
A notable feature of this model is its output efficiency. While it processes reasoning internally, it is trained to deliver direct and concise final answers without generating verbose explanations. This approach reduces token usage and improves response speed, making it highly efficient for QA and automated evaluation environments.
The V3 model recorded improved scores in Korean reasoning and knowledge evaluation metrics compared to the base model. The average score based on the recent official AI Hub submission is 0.560.
| Model | Avg | KMMLU-Pro | CLIcK | HLE(Ko) | MuSR(Ko) | Com2-main | KMMLU | COPA | HellaSwag |
|---|---|---|---|---|---|---|---|---|---|
| QuettaLLMs-27B-Koreasoner-V3 | 0.560 | 0.676 | 0.794 | 0.070 | 0.604 | 0.654 | 73.8 | 81.9 | 52.4 |
| Qwen3.5-27B (Base) | 0.530 | 0.622 | 0.770 | 0.058 | 0.577 | 0.622 | 68.50 | 79.3 | 49.0 |
Result Summary: Metrics such as KMMLU-Pro (+0.054), CLIcK (+0.024), and KMMLU (+5.30p) increased compared to the base model, indicating an overall improvement in Korean domain knowledge and context understanding.
The model was trained using datasets collected from external sources, which were then filtered for noise and refined according to internal standards.
Over 230,000 refined SFT samples were used. The main components are: