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 commited on 19 days ago

Commit

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+---
+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 계승**되며,
+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
+```python
+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](https://huggingface.co/FINAL-Bench/Darwin-27B-Opus)
+- 가문: Darwin family (Darwin V7 진화적 머지 시리즈)