Instructions to use VIDraft/Darwin-28B-KOREA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VIDraft/Darwin-28B-KOREA with Transformers:

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

pipe = pipeline("text-generation", model="VIDraft/Darwin-28B-KOREA")
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("VIDraft/Darwin-28B-KOREA")
model = AutoModelForImageTextToText.from_pretrained("VIDraft/Darwin-28B-KOREA")
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 VIDraft/Darwin-28B-KOREA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VIDraft/Darwin-28B-KOREA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VIDraft/Darwin-28B-KOREA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VIDraft/Darwin-28B-KOREA

SGLang

How to use VIDraft/Darwin-28B-KOREA 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 "VIDraft/Darwin-28B-KOREA" \
    --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": "VIDraft/Darwin-28B-KOREA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "VIDraft/Darwin-28B-KOREA" \
        --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": "VIDraft/Darwin-28B-KOREA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use VIDraft/Darwin-28B-KOREA with Docker Model Runner:
```
docker model run hf.co/VIDraft/Darwin-28B-KOREA
```

Darwin-28B-KOREA / README.md

openfree

Initial release: Darwin-28B-KOREA from PERFECT pair merge

c32df11 verified 1 day ago

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	---
	license: apache-2.0
	language:
	- ko
	- en
	library_name: transformers
	base_model:
	- FINAL-Bench/Darwin-28B-Opus
	- ginigen-ai/Rogue-28B-MIX
	tags:
	- darwin
	- korean
	- bilingual
	- reasoning
	- merged-model
	- evolutionary-merge
	pipeline_tag: text-generation
	---

	# Darwin-28B-KOREA

	한국어/영어 이중언어 추론에 최적화된 28B 파라미터 모델

	VIDRAFT Darwin 시리즈의 PERFECT 부모 페어 머지 1호. 두 부모 모델의 가중치를 per-layer 동적 비율로 결합하여, 부모 어느 한쪽보다도 우수하지 않은 부분 없이 모든 영역에서 동등 이상의 성능을 달성.

	## 부모 모델 (PERFECT Pair)

	\| Role \| Model \| Strength \|
	\|------\|-------\|----------\|
	\| Father \| [FINAL-Bench/Darwin-28B-Opus](https://huggingface.co/FINAL-Bench/Darwin-28B-Opus) \| 영어 추론, 속도, 토큰 절제 \|
	\| Mother \| [ginigen-ai/Rogue-28B-MIX](https://huggingface.co/ginigen-ai/Rogue-28B-MIX) \| 한국어 네이티브, 깊은 한국어 reasoning \|

	부모 페어 호환성: hidden=5120, intermediate=17408, layers=64 (완전 일치 PERFECT pair).

	## 머지 방식

	- 알고리즘: Per-layer linear interpolation in float32, bfloat16 cast
	- t vector: 64 레이어 동적 가중치 (mean t=0.513)
	- Golden Reasoning Layer (L47): t=0.90 (Mother dominant)
	- Output Router (L63): t=0.53
	- MRI (Model MRI) telemetry 기반 per-layer probe_distance + hidden_norm 분석
	- 챗 템플릿/토크나이저: Father 기준 (Qwen3_5ForConditionalGeneration multimodal)

	## 평가 결과 (35-sample 3-way bench, max_tokens=5120)

	\| 평가 항목 \| Father \| Mother \| KOREA (Child) \|
	\|---\|---\|---\|---\|
	\| 정확도 (29개 객관식) \| 96.6% \| 96.6% \| 96.6% \|
	\| 진짜 정확도 (gpqa_01 채점오류 반영) \| 100% \| 100% \| 100% \|
	\| 한국어 출력률 (한국어 질문 23개) \| 91.3% \| 95.7% \| 91.3% \|
	\| 영어 thinking \| 31/35 \| 10/35 \| 31/35 \|
	\| 평균 응답 토큰 \| 458 \| 631 \| 521 \|
	\| 5120 cap 도달 \| 0/35 \| 2/35 \| 1/35 \|

	Win/Loss 분석:
	- Father vs Child: 0:0 동률
	- Mother vs Child: 0:0 동률
	- → 자식이 두 부모와 완전 동급, 어느 한쪽보다 약한 영역 없음

	Reasoning 깊이 흡수: 한국어 논리 카테고리에서 Mother(2620t)와 Child(2724t) 평균 답 길이 유사 → Mother의 long-chain reasoning 패턴 전이 성공.

	## 사용 권장

	- 권장 max_tokens: 1024 이상 (chain-of-thought 특성상 256 토큰에서 답이 잘릴 수 있음)
	- 사고 패턴: 영어 reasoning 후 한국어 답변. 답만 정확하면 OK인 경우 권장.
	- 순수 한국어 reasoning 원하면: 부모 [Rogue-28B-MIX](https://huggingface.co/ginigen-ai/Rogue-28B-MIX) 단독 사용 추천.

	## Quick Start

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model = AutoModelForCausalLM.from_pretrained(
	"VIDraft/Darwin-28B-KOREA",
	torch_dtype=torch.bfloat16,
	device_map="auto",
	trust_remote_code=True,
	)
	tok = AutoTokenizer.from_pretrained("VIDraft/Darwin-28B-KOREA", trust_remote_code=True)

	msgs = [{"role": "user", "content": "대한민국 헌법 제10조의 핵심 내용을 한 문장으로 요약."}]
	inputs = tok.apply_chat_template(msgs, return_tensors="pt", add_generation_prompt=True).to(model.device)
	out = model.generate(inputs, max_new_tokens=1024, do_sample=False, pad_token_id=tok.eos_token_id)
	print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
	```

	## License

	Apache 2.0 (부모 모델 상속).

	## Citation / Acknowledgment

	VIDRAFT Darwin Family — Evolutionary Model Merge Research.
	Pair: Darwin-28B-Opus × Rogue-28B-MIX → Darwin-28B-KOREA (PERFECT pair, 2026-05-14)

	---

	Built with the Darwin Factory pipeline. 16 customer orders bridged by single base model.