Instructions to use ginigen-ai/Rogue-28B-MIX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ginigen-ai/Rogue-28B-MIX with Transformers:

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

pipe = pipeline("image-text-to-text", model="ginigen-ai/Rogue-28B-MIX")
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("ginigen-ai/Rogue-28B-MIX")
model = AutoModelForMultimodalLM.from_pretrained("ginigen-ai/Rogue-28B-MIX", 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 ginigen-ai/Rogue-28B-MIX with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ginigen-ai/Rogue-28B-MIX"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ginigen-ai/Rogue-28B-MIX",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/ginigen-ai/Rogue-28B-MIX

SGLang

How to use ginigen-ai/Rogue-28B-MIX 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 "ginigen-ai/Rogue-28B-MIX" \
    --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": "ginigen-ai/Rogue-28B-MIX",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "ginigen-ai/Rogue-28B-MIX" \
        --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": "ginigen-ai/Rogue-28B-MIX",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use ginigen-ai/Rogue-28B-MIX with Docker Model Runner:
```
docker model run hf.co/ginigen-ai/Rogue-28B-MIX
```

Rogue-28B-MIX

한국어 reasoning + multimodal mix 모델.

🏛️ 가문 계보

┌──────────────────────────────────────────┐
│  증조부 (Great-Grandfather)               │
│  Qwen-3.6-27B                             │
└──────────────────────────────────────────┘
                  │
                  ▼
┌──────────────────────────────────────────┐
│  조부 (Grandfather)                       │
│  Darwin-3.6-28B                           │
└──────────────────────────────────────────┘
                  │
                  ▼
┌──────────────────────────────────────────┐
│  아빠 (Father)                            │
│  FINAL-Bench/Darwin-28B-KR                │
│  - 한국어 특화 reasoning 모델              │
└──────────────────────────────────────────┘
                  │
                  ×× 교배 ××
                  │
┌──────────────────────────────────────────┐
│  엄마 (Mother)                            │
│  NewenAI/QuettaLLMs-27B-Koreasoner-V3     │
│  - K-AI Leaderboard 1위                   │
└──────────────────────────────────────────┘
                  │
                  ▼
╔══════════════════════════════════════════╗
║  자식 (Child) — 본 모델                    ║
║  ginigen-ai/Rogue-28B-MIX                 ║
║                                            ║
║  - 친가의 reasoning 계승                   ║
║  - 외가의 한국어 K-AI 지식 계승            ║
║  - <think> 추론 트레이스 보존              ║
║  - 멀티모달 헤드 보존                      ║
╚══════════════════════════════════════════╝

🎓 학습 개요

친가 × 외가 모델 가중치 머지
K-AI 도메인 데이터로 추가 SFT
K-AI Leaderboard Docker 호환 형식 정비

📊 평가

한국어 공개 10 데이터셋, 100문제 × 1 seed.

Dataset	Rogue-28B-MIX	엄마(Quetta)
CLIcK	84%	85%
KMMLU History	48% 🏆	45%
KMMLU Law	25%	26%
KMMLU Health	81% 🏆	80%
HAERAE GK	63%	66%
HAERAE History	89%	90%
HAERAE Linguistics	90%	95%
KoBEST Hellaswag	95%	97%
KoBEST COPA	98%	99%
KoBEST BoolQ	97%	97%
Macro Avg	77.0%	78.0%

K-AI Leaderboard 핵심 카테고리(의료·역사)에서 엄마 추월.

🎯 사용법

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "ginigen-ai/Rogue-28B-MIX"
tokenizer = AutoTokenizer.from_pretrained(
    model_id, trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

prompt = "한국의 추석에 대해 설명해주세요."
messages = [{"role": "user", "content": prompt}]
inputs = tokenizer.apply_chat_template(
    messages, return_tensors="pt", add_generation_prompt=True
)
out = model.generate(
    inputs.to(model.device),
    max_new_tokens=512,
    do_sample=False,
)
print(tokenizer.decode(out[0], skip_special_tokens=False))

🛠️ 사양

파라미터: 28B (multimodal)
양자화: bf16
컨텍스트: 8K (확장 가능)
언어: 한국어 + 영어
추론: <think> reasoning trace
License: Apache 2.0

🤝 출처

아빠: FINAL-Bench/Darwin-28B-KR
엄마: NewenAI/QuettaLLMs-27B-Koreasoner-V3

학습 데이터셋

한국지능정보사회진흥원(NIA) AI Hub 한국어 대용량 말뭉치를 기반으로 학습하였습니다.

📂 한국어 대화 데이터셋
https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=272

📂 의료·법률 전문 서적 말뭉치
https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=71487

📂 국가기록물 초거대 AI 학습 말뭉치
https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=71788

📂 금융·법률 문서 기계독해 데이터
https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=71610

📂 멀티세션·대화 관련 말뭉치 데이터
https://aihub.or.kr/aihubdata/data/view.do?currMenu=511&topMenu=100&aihubDataSe=dataPckage&dataPckageSn=1

📂 전문 의학지식 데이터
https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=71874

📂 한국어 생성 기반 상식추론 데이터셋
https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=459

Downloads last month: 14

Safetensors

Model size

28B params

Tensor type

BF16

Model tree for ginigen-ai/Rogue-28B-MIX

Base model

FINAL-Bench/Darwin-27B-Opus

Finetuned

FINAL-Bench/Darwin-27B-KR

Finetuned

FINAL-Bench/Darwin-28B-KR

Finetuned

(3)

this model

Finetunes

1 model

ginigen-ai
/

Rogue-28B-MIX

Rogue-28B-MIX

🏛️ 가문 계보

🎓 학습 개요

📊 평가

🎯 사용법

🛠️ 사양

🤝 출처

학습 데이터셋

Model tree for ginigen-ai/Rogue-28B-MIX

Space using ginigen-ai/Rogue-28B-MIX 1