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 18 days ago

Commit

a781786

verified ·

1 Parent(s): 698cdd0

fix lineage: Qwen-3.5-27B (great-gf) / Darwin-27B-Opus (gf)

Browse files

Files changed (1) hide show

README.md +12 -10

README.md CHANGED Viewed

@@ -44,25 +44,27 @@ base_model:
 ```
 ┌──────────────────────────────────────────┐
 │  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 ××
@@ -167,6 +169,6 @@ print(tokenizer.decode(out[0], skip_special_tokens=False))
 ## 🤝 출처
-- 아빠: [FINAL-Bench/Darwin-27B-Opus](https://huggingface.co/FINAL-Bench/Darwin-27B-Opus)
 - 엄마: [NewenAI/QuettaLLMs-27B-Koreasoner-V3](https://huggingface.co/NewenAI/QuettaLLMs-27B-Koreasoner-V3)
 - 가문: Darwin family (Darwin V7 진화적 머지 시리즈)

 ```
 ┌──────────────────────────────────────────┐
 │  증조부 (Great-Grandfather)               │
+│  Qwen-3.5-27B                             │
+│  - 멀티모달 28B 베이스                     │
 └──────────────────────────────────────────┘
                   │
                   ▼ Darwin V7 진화 머지
 ┌──────────────────────────────────────────┐
 │  조부 (Grandfather)                       │
+│  FINAL-Bench/Darwin-27B-Opus              │
+│  - Darwin V7 진화의 정점                   │
+│  - GPQA 88.4% reasoning                   │
+│  - <think> 트레이스 패턴                   │
 └──────────────────────────────────────────┘
                   │
+                  ▼ 한국어 특화 진화
 ╔══════════════════════════════════════════╗
 ║  아빠 (Father)                            ║
+║  Darwin family Korean 직계                 ║
 ║                                            ║
+║  - Darwin-27B-Opus의 한국어 특화 후손      ║
+║  - reasoning DNA 보존                       ║
+║  - <think> 패턴 유지                        ║
 ╚══════════════════════════════════════════╝
                   │
                   ×× 다윈 교배 ××
 ## 🤝 출처
+- 조부: [FINAL-Bench/Darwin-27B-Opus](https://huggingface.co/FINAL-Bench/Darwin-27B-Opus)
 - 엄마: [NewenAI/QuettaLLMs-27B-Koreasoner-V3](https://huggingface.co/NewenAI/QuettaLLMs-27B-Koreasoner-V3)
 - 가문: Darwin family (Darwin V7 진화적 머지 시리즈)