Instructions to use Warecube/Warecube-KO-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Warecube/Warecube-KO-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Warecube/Warecube-KO-27B")
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("Warecube/Warecube-KO-27B")
model = AutoModelForImageTextToText.from_pretrained("Warecube/Warecube-KO-27B")
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 Warecube/Warecube-KO-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Warecube/Warecube-KO-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Warecube/Warecube-KO-27B",
		"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/Warecube/Warecube-KO-27B

SGLang

How to use Warecube/Warecube-KO-27B 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 "Warecube/Warecube-KO-27B" \
    --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": "Warecube/Warecube-KO-27B",
		"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 "Warecube/Warecube-KO-27B" \
        --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": "Warecube/Warecube-KO-27B",
		"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 Warecube/Warecube-KO-27B with Docker Model Runner:
```
docker model run hf.co/Warecube/Warecube-KO-27B
```

Warecube-KO-27B / README.md

Warecube

upload README.md

aaf9908 verified 26 days ago

5.36 kB

license: apache-2.0
language:
  - ko
  - en
library_name: transformers
tags:
  - korean
  - reasoning
  - darwin
  - evolutionary-merge
base_model:
  - FINAL-Bench/Darwin-27B-Opus

Warecube-KO-27B

한국어 reasoning 모델 — Darwin 진화적 머지 기반.

🧬 Darwin 진화 컨셉

본 모델은 Darwin V7 진화적 모델 머지(Evolutionary Model Merge) 패러다임으로 제작되었습니다.

   자연 진화                    Darwin 머지
   ─────────                    ───────────
   유전자 교차 (crossover)  →   가중치 모듈별 비율 결합
   자연 선택 (selection)    →   적합도 평가 후 최적 후손 선별
   세대 진화 (generations)  →   다세대 머지·정제 반복
   적자 생존                →   K-AI 도메인 우수 자손만 보존

부모의 능력이 자식 모델로 유전적으로 계승되며, 세대를 거쳐 한국어·추론·문화 지능이 진화합니다.

🏛️ 가문 계보

┌──────────────────────────────────────────┐
│  증조부 (Great-Grandfather)               │
│  Qwen-3.6-27B                             │
└──────────────────────────────────────────┘
                  │
                  ▼ Darwin V7 진화 머지
┌──────────────────────────────────────────┐
│  조부 (Grandfather)                       │
│  Darwin-3.6-28B                           │
│  - Qwen 3.6 진화 머지의 정점               │
└──────────────────────────────────────────┘
                  │
                  ▼ 한국어·추론 특화 진화
╔══════════════════════════════════════════╗
║  아빠 (Father)                            ║
║  FINAL-Bench/Darwin-27B-Opus              ║
║                                            ║
║  - Darwin family reasoning 정점             ║
║  - GPQA 88.4% (영어 추론)                   ║
║  - <think> 트레이스 패턴 보유                ║
║  - Apache 2.0                              ║
╚══════════════════════════════════════════╝
                  │
                  ×× 다윈 교배 ××
                  │
                  ▼ Darwin 진화적 머지 + 한국어 정제
╔══════════════════════════════════════════╗
║  자식 (Child) — 본 모델                    ║
║  Warecube/Warecube-KO-27B                 ║
║                                            ║
║  ✦ 아빠의 reasoning DNA 직접 계승          ║
║  ✦ 한국어 표현·지식 진화 강화               ║
║  ✦ <think> 추론 트레이스 보존              ║
║  ✦ K-AI 도메인 적합도 진화                  ║
╚══════════════════════════════════════════╝

🎓 진화 단계

Stage	개략
1. 교배 (Crossover)	친가 가중치를 모듈별 비율로 진화 머지
2. 선택 (Selection)	한국어 도메인 적합도 평가로 우수 후손 선별
3. 정제 (Refinement)	한국어 instruction 데이터로 추가 진화
4. 적응 (Adaptation)	K-AI Leaderboard Docker 호환 형식으로 정비

🎯 사용법

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Warecube/Warecube-KO-27B"
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: 27B (text)
양자화: bf16
컨텍스트: 8K (확장 가능)
언어: 한국어 + 영어
추론: <think> reasoning trace
License: Apache 2.0

📊 평가

한국어 공개 10 데이터셋, 100문제 × 1 seed.

Dataset	Score
CLIcK	87%
KMMLU History	50%
KMMLU Law	29%
KMMLU Health	78%
HAERAE General	58%
HAERAE History	86%
HAERAE Linguistics	89%
KoBEST Hellaswag	89%
KoBEST COPA	100%
KoBEST BoolQ	97%
Macro Avg	76.3%

🤝 출처

아빠: FINAL-Bench/Darwin-27B-Opus
가문: Darwin family (Darwin V7 진화적 머지 시리즈)