Text Generation
Transformers
Safetensors
Korean
English
qwen3_5
image-text-to-text
darwin
korean
reasoning
multimodal
qwen3.5
evolutionary-merge
vidraft
conversational
Instructions to use FINAL-Bench/Darwin-28B-KR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FINAL-Bench/Darwin-28B-KR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FINAL-Bench/Darwin-28B-KR") 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("FINAL-Bench/Darwin-28B-KR") model = AutoModelForImageTextToText.from_pretrained("FINAL-Bench/Darwin-28B-KR") 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 FINAL-Bench/Darwin-28B-KR with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FINAL-Bench/Darwin-28B-KR" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FINAL-Bench/Darwin-28B-KR", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/FINAL-Bench/Darwin-28B-KR
- SGLang
How to use FINAL-Bench/Darwin-28B-KR 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 "FINAL-Bench/Darwin-28B-KR" \ --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": "FINAL-Bench/Darwin-28B-KR", "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 "FINAL-Bench/Darwin-28B-KR" \ --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": "FINAL-Bench/Darwin-28B-KR", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use FINAL-Bench/Darwin-28B-KR with Docker Model Runner:
docker model run hf.co/FINAL-Bench/Darwin-28B-KR
File size: 3,848 Bytes
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license: apache-2.0
language:
- en
- ko
base_model:
- FINAL-Bench/Darwin-28B-Opus
- FINAL-Bench/Darwin-27B-KR
pipeline_tag: text-generation
tags:
- darwin
- korean
- reasoning
- multimodal
- qwen3.5
- evolutionary-merge
library_name: transformers
---
# Darwin-28B-KR
> **Darwin family 한국어 특화 2세대 모체 모델**
> 28B 영어 추론력과 27B 한국어 능력을 통합한 Darwin V7 진화 머지 결과물.
## 🎯 모델 포지셔닝
Darwin-28B-KR은 Darwin family에서 **한국어 특화 2세대 모델 개발의 모체(母體)** 로 설계되었습니다.
이 모델 자체로 사용 가능하며, 향후 다양한 한국어 도메인 특화 모델(법률·의료·금융·학술 등)의 **공통 출발점**이 됩니다.
## 🧬 Lineage
```
Qwen3.5-27B (Alibaba Qwen team)
│
▼
Darwin-27B-Opus (FINAL-Bench)
│ Darwin V7 evolutionary merge
│
┌───┴────────────────────────┐
▼ ▼
Darwin-28B-Opus Darwin-27B-KR
(English/reasoning (Korean-specialized
+ multimodal) champion)
│ │
└────────┬───────────────────┘
│ Darwin V7 MRI-aware merge
▼
Darwin-28B-KR ← this model (2nd-gen mother)
```
## ⚙️ 구성 능력
| 능력 | 출처 | 강도 |
|---|---|---|
| 한국어 이해/생성 | Darwin-27B-KR 계열 | ⭐⭐⭐⭐⭐ |
| 영어 추론 | Darwin-28B-Opus 계열 | ⭐⭐⭐⭐ |
| 멀티모달 (이미지/비디오) | Darwin-28B-Opus 보존 | ⭐⭐⭐⭐ |
| 한국어 추론 (CSAT/PSAT) | 통합 효과 | ⭐⭐⭐⭐⭐ |
| 영한 코드스위칭 | 통합 효과 | ⭐⭐⭐⭐ |
## 📊 Specs
| | |
|---|---|
| Architecture | Qwen3_5ForConditionalGeneration (hybrid full + linear attention) |
| Parameters | ~28B |
| Hidden size | 5120 |
| Layers | 64 |
| Vocab size | 248,320 |
| Format | bfloat16 (52 GB on disk) |
| Context | 8K~32K (deployment dependent) |
## 🚀 Usage
### vLLM (recommended)
```bash
vllm serve FINAL-Bench/Darwin-28B-KR \
--trust-remote-code \
--port 8000 \
--enforce-eager \
--max-model-len 8192 \
--gpu-memory-utilization 0.85
```
### OpenAI-compatible client
```python
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
response = client.chat.completions.create(
model="FINAL-Bench/Darwin-28B-KR",
messages=[{"role": "user", "content": "한국의 광복절은 무엇을 기념하는 날인가요?"}],
max_tokens=2048,
temperature=0.0,
)
```
## 🖥️ Hardware
| GPU family | Status |
|---|---|
| NVIDIA Blackwell (B200) | ✅ Best |
| NVIDIA Hopper (H100/H200) | ✅ Recommended |
| NVIDIA Ada (L40S) | ⚠️ Marginal (52 GB BF16) |
| Older Ampere | ❌ Insufficient VRAM |
**Minimum VRAM**: ~55 GB for inference at BF16.
## 🌳 2세대 도메인 특화 모델 개발 (예정)
이 모체에서 파생될 예정인 한국어 특화 변종들:
- **Darwin-28B-KR-Legal** — 법률 도메인 SFT
- **Darwin-28B-KR-Medical** — 의료 도메인 SFT
- **Darwin-28B-KR-Finance** — 금융 도메인 SFT
- **Darwin-28B-KR-Code** — 한국어 주석 코드 생성
- **Darwin-28B-KR-MFP4** — 메모리 효율 양자화 버전
각 변종은 이 모델을 base로 하여 도메인 데이터로 미세조정/머지됩니다.
## 🙏 Credits
- Architecture lineage: Qwen3.5 (Alibaba Qwen team)
- Father: [FINAL-Bench/Darwin-28B-Opus](https://huggingface.co/FINAL-Bench/Darwin-28B-Opus)
- Mother: [FINAL-Bench/Darwin-27B-KR](https://huggingface.co/FINAL-Bench/Darwin-27B-KR)
- Merge methodology: Darwin V7 MRI-aware evolutionary merge
## 📜 License
Apache 2.0 (inherited from base models).
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