Rogue-27B-KR — Korean-Specialized High-Performance Reasoning Model

Qwen3.5 Hybrid Architecture | ~26B Params | Thinking Mode | 262K Context | BF16 | Apache 2.0 Darwin-27B-Opus(Father) + Korean-SFT Qwen 27B(Mother) Merge + Korean SFT

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K-AI Leaderboard — 2nd Place

K-AI Leaderboard is operated by the Korean Ministry of Science and ICT (MSIT) / NIA, evaluating Korean AI language models across multiple benchmarks.

Rogue-27B-KR achieved 2nd place on the K-AI Leaderboard with an average score of 0.549.

Metric	Score
Average	0.549
KMMLU-Pro	0.658
CLIcK	0.794
HLE(Ko)	0.07
MuSR(Ko)	0.584
Com2-main(Ko)	0.646

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Model Overview

Rogue-27B-KR is a Korean-specialized high-performance reasoning model:

• Father: FINAL-Bench/Darwin-27B-Opus — Evolutionary merge model based on Qwen3.5-27B (inheriting Claude 4.6 Opus reasoning patterns)
• Mother: Korean-specialized SFT applied Qwen 27B variant
• Rogue: Merged both parents + additional Korean SFT

Combines Darwin's powerful reasoning with the mother model's Korean expressiveness, greatly enhancing Korean intelligence across culture, language, law, history, and more.

Key Features

• K-AI Leaderboard 2nd Place — Top-tier Korean AI performance verified by government evaluation
• Strong reasoning — Chain-of-Thought inherited from Darwin-27B-Opus
• Enhanced Korean cultural intelligence — +6%p improvement on CLIcK benchmark (0.794)
• 262K context — Ultra-long Korean document processing
• Thinking mode — Step-by-step reasoning via <think> tags
• BF16 format — Memory-efficient (~48GB), optimized for modern GPUs
• Apache 2.0 — Free for commercial use

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Benchmark Results

CLIcK (Cultural and Linguistic Intelligence in Korean)

200 questions, 0-shot evaluation:

Model	CLIcK (Overall)	Culture	Language
Qwen3.5-27B (base)	69.52%	71.84%	64.66%
Darwin-27B-Opus	70.19%	72.91%	64.47%
Rogue-27B-KR	75.59%	77.85%	70.86%

+6.07%p over Qwen3.5-27B, +5.40%p over Darwin-27B-Opus

Category Details

Category	Qwen3.5-27B	Darwin-27B-Opus	Rogue-27B-KR
Economy	93.22%	93.22%	94.92%
Geography	70.23%	70.23%	75.57%
History	47.00%	47.00%	53.50%
K-pop	92.68%	97.56%	92.68%
Law	59.50%	60.00%	69.50%
Politics	80.95%	82.14%	85.71%
Society	87.00%	89.00%	90.00%
Tradition	81.50%	82.50%	88.50%
Function Words	68.18%	67.42%	75.00%
Grammar	44.50%	44.50%	53.00%
Text	82.50%	82.50%	86.00%

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Model Specifications

Property	Value
Architecture	Qwen3.5 (GatedDeltaNet Hybrid Attention, 64-layer)
Parameters	~26B (text-only, no vision encoder)
Hidden Size	5120
Intermediate Size	16384
Layers	64
Attention Heads	24 (GQA, KV Heads: 4)
Context Length	262,144 tokens (1M with YaRN)
Precision	BF16 (~48GB)
Vocab Size	248,320
Thinking	Supported (<think> tags)
Languages	Korean, English, Japanese, Chinese + 201 languages
License	Apache 2.0

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VRAM Requirements

Setup	VRAM	Notes
BF16 (native)	~48 GB	Single H100/B200 or 2x A100
4-bit quantized	~14 GB	Single RTX 4090
8-bit quantized	~26 GB	Single A6000

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Usage

Requirements: transformers >= 4.57.0 (Qwen3.5 support). Latest recommended: pip install -U transformers

Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("ginigen-ai/Rogue-27B-KR")
model = AutoModelForCausalLM.from_pretrained(
    "ginigen-ai/Rogue-27B-KR",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [{"role": "user", "content": "Explain traditional Korean wedding ceremonies."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=4096, do_sample=False)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

vLLM

vllm serve ginigen-ai/Rogue-27B-KR \
    --enforce-eager \
    --max-model-len 32768 \
    --dtype bfloat16

SGLang

python -m sglang.launch_server \
    --model-path ginigen-ai/Rogue-27B-KR \
    --tp 1 \
    --mem-fraction-static 0.90 \
    --context-length 32768 \
    --dtype bfloat16 \
    --reasoning-parser qwen3

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Training Info

Item	Details
Father model	FINAL-Bench/Darwin-27B-Opus — Reasoning
Mother model	Korean-SFT Qwen 27B variant — Korean expressiveness
Merge	Weight merge (intermediate_size 16384 adopted)
Additional SFT	Korean-specialized Supervised Fine-Tuning
Developer	GinigenAI

Lineage

Qwen/Qwen3.5-27B (Apache 2.0)
+-- FINAL-Bench/Darwin-27B-Opus (evolutionary merge)  <- Father
|
+-- Korean-SFT Qwen 27B (Korean specialization)       <- Mother
        |
    [Merge: Father + Mother]
        |
    [Additional Korean SFT]
        |
    ginigen-ai/Rogue-27B-KR (this model, BF16)

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Citation

@misc{ginigen_rogue_27b_kr_2026,
  title        = {Rogue-27B-KR: Korean-Specialized High-Performance Reasoning Model},
  author       = {GinigenAI},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/ginigen-ai/Rogue-27B-KR}}
}

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Acknowledgements

• VIDRAFT — Darwin-27B-Opus base model
• Qwen Team — Qwen3.5 architecture
• Korean Ministry of Science and ICT / NIA — K-AI Leaderboard

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