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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("ginigen-ai/Rogue-28B-MIX")
model = AutoModelForImageTextToText.from_pretrained("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?"}
        ]
    },
]
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

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 / README.md

ginigen-ai

fix lineage: Qwen-3.6-27B (great-gf) / Darwin-3.6-28B (gf) / Darwin-28B-KR (father), narrow boxes, no trade secrets

8f9b2f1 verified 19 days ago

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	---
	license: apache-2.0
	language:
	- ko
	- en
	library_name: transformers
	tags:
	- korean
	- reasoning
	- multimodal
	- mix
	base_model:
	- FINAL-Bench/Darwin-28B-KR
	- NewenAI/QuettaLLMs-27B-Koreasoner-V3
	---

	# 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

	1. 친가 × 외가 모델 가중치 머지
	2. K-AI 도메인 데이터로 추가 SFT
	3. 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

	```python
	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](https://huggingface.co/FINAL-Bench/Darwin-28B-KR)
	- 엄마: [NewenAI/QuettaLLMs-27B-Koreasoner-V3](https://huggingface.co/NewenAI/QuettaLLMs-27B-Koreasoner-V3)