Instructions to use VIDraft/Darwin-9B-KOREA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VIDraft/Darwin-9B-KOREA with Transformers:

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

pipe = pipeline("text-generation", model="VIDraft/Darwin-9B-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-9B-KOREA")
model = AutoModelForImageTextToText.from_pretrained("VIDraft/Darwin-9B-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-9B-KOREA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VIDraft/Darwin-9B-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-9B-KOREA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VIDraft/Darwin-9B-KOREA

SGLang

How to use VIDraft/Darwin-9B-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-9B-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-9B-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-9B-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-9B-KOREA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use VIDraft/Darwin-9B-KOREA with Docker Model Runner:
```
docker model run hf.co/VIDraft/Darwin-9B-KOREA
```

Darwin-9B-KOREA / README.md

openfree

Initial release: Darwin-9B-KOREA from PERFECT pair (Opus x NEG)

1c0db2c verified 1 day ago

preview code

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metadata

license: apache-2.0
language:
  - ko
  - en
library_name: transformers
base_model:
  - FINAL-Bench/Darwin-9B-Opus
  - FINAL-Bench/Darwin-9B-NEG
tags:
  - darwin
  - korean
  - bilingual
  - reasoning
  - merged-model
  - evolutionary-merge
pipeline_tag: text-generation

Darwin-9B-KOREA

한국어/영어 이중언어 추론에 최적화된 9B 파라미터 모델 (28B-KOREA의 경량 자매 모델)

VIDRAFT Darwin 시리즈의 PERFECT 부모 페어 머지. Darwin-9B-Opus(정) × Darwin-9B-NEG(반) 페어를 per-layer 동적 비율로 결합하여, 28B-KOREA 대비 1/2.7 사이즈로 거의 동등한 한국어 능력을 달성.

부모 모델 (PERFECT Pair)

Role	Model	Strength
Father	FINAL-Bench/Darwin-9B-Opus	영어 reasoning, 한국어 답변
Mother	FINAL-Bench/Darwin-9B-NEG	Negative SFT, 정/반 합성

페어 호환성: hidden=4096, intermediate=12288, layers=32, vocab=248320 — 100% 일치 PERFECT pair.

머지 방식

알고리즘: Per-layer linear interpolation (float32 → bfloat16)
t vector: 32 레이어 동적 가중치 (mean t=0.513)
- Golden Reasoning Layer (L0): t=0.90 (28B 페어는 L47, 9B는 L0이 입력 직후 가장 reasoning critical)
- Output Router (L31): t=0.53
챗 템플릿/토크나이저: Father 기준

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

평가 항목	Father (9B-Opus)	Mother (9B-NEG)	KOREA (Child)
정확도 (29개 객관식)	93.1%	93.1%	93.1%
한국어 출력률 (한국어 질문 23개)	91.3%	87.0%	91.3% ⭐

Win/Loss: Father vs Child 0:0, Mother vs Child 0:0 — 완전 동률 (자식이 두 부모와 동급)

28B-KOREA 비교: 정확도 -3.5%p (96.6% → 93.1%, 모델 크기 차이), 한국어 출력률 동일 91.3%.

사용 권장

권장 max_tokens: 1024 이상 (chain-of-thought 특성)
GPU 메모리: ~20GB BF16 (단일 H100/H200/A100 80GB 충분)
사고 패턴: 영어 reasoning 후 한국어 답변. 답 정확도 우선이면 권장.

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "VIDraft/Darwin-9B-KOREA",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
tok = AutoTokenizer.from_pretrained("VIDraft/Darwin-9B-KOREA", trust_remote_code=True)

msgs = [{"role": "user", "content": "한국 최초 한글 창제 이유를 한 문장으로."}]
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).

Related Models

VIDraft/Darwin-28B-KOREA — 큰 자매 모델 (52GB, 96.6% 정확도)

VIDRAFT Darwin Factory — Pair: Darwin-9B-Opus × Darwin-9B-NEG → Darwin-9B-KOREA