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

698cdd0

verified ·

1 Parent(s): 0995dda

add Mother to lineage (NewenAI/QuettaLLMs-27B-Koreasoner-V3)

Browse files

Files changed (1) hide show

README.md +15 -3

README.md CHANGED Viewed

@@ -11,6 +11,7 @@ tags:
 - evolutionary-merge
 base_model:
 - FINAL-Bench/Darwin-27B-Opus
 ---
 # Warecube-KO-27B
@@ -66,13 +67,23 @@ base_model:
                   │
                   ×× 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                  ║
 ╚══════════════════════════════════════════╝
@@ -84,7 +95,7 @@ base_model:
 | 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 |
@@ -157,4 +168,5 @@ print(tokenizer.decode(out[0], skip_special_tokens=False))
 ## 🤝 출처
 - 아빠: [FINAL-Bench/Darwin-27B-Opus](https://huggingface.co/FINAL-Bench/Darwin-27B-Opus)
 - 가문: Darwin family (Darwin V7 진화적 머지 시리즈)

 - evolutionary-merge
 base_model:
 - FINAL-Bench/Darwin-27B-Opus
+- NewenAI/QuettaLLMs-27B-Koreasoner-V3
 ---
 # Warecube-KO-27B
                   │
                   ×× 다윈 교배 ××
                   │
+╔══════════════════════════════════════════╗
+║  엄마 (Mother)                            ║
+║  NewenAI/QuettaLLMs-27B-Koreasoner-V3     ║
+║                                            ║
+║  - 한국어 SOTA 모델                          ║
+║  - K-AI Leaderboard 1위 (avg 0.560)        ║
+║  - 한국어 도메인 SFT 정제                   ║
+║  - Apache 2.0                              ║
+╚══════════════════════════════════════════╝
+                  │
                   ▼ Darwin 진화적 머지 + 한국어 정제
 ╔══════════════════════════════════════════╗
 ║  자식 (Child) — 본 모델                    ║
 ║  Warecube/Warecube-KO-27B                 ║
 ║                                            ║
+║  ✦ 아빠의 reasoning DNA 계승                ║
+║  ✦ 엄마의 한국어 표현·지식 계승              ║
 ║  ✦ <think> 추론 트레이스 보존              ║
 ║  ✦ K-AI 도메인 적합도 진화                  ║
 ╚══════════════════════════════════════════╝
 | Stage | 개략 |
 |:---|:---|
+| **1. 교배 (Crossover)** | 친가·외가 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 |
 ## 🤝 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)