Instructions to use JGOS-Model/JGOS-31B-Citizen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JGOS-Model/JGOS-31B-Citizen with Transformers:

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

pipe = pipeline("image-text-to-text", model="JGOS-Model/JGOS-31B-Citizen")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("JGOS-Model/JGOS-31B-Citizen")
model = AutoModelForMultimodalLM.from_pretrained("JGOS-Model/JGOS-31B-Citizen")
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 Settings

vLLM

How to use JGOS-Model/JGOS-31B-Citizen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JGOS-Model/JGOS-31B-Citizen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JGOS-Model/JGOS-31B-Citizen",
		"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/JGOS-Model/JGOS-31B-Citizen

SGLang

How to use JGOS-Model/JGOS-31B-Citizen 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 "JGOS-Model/JGOS-31B-Citizen" \
    --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": "JGOS-Model/JGOS-31B-Citizen",
		"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 "JGOS-Model/JGOS-31B-Citizen" \
        --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": "JGOS-Model/JGOS-31B-Citizen",
		"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 JGOS-Model/JGOS-31B-Citizen with Docker Model Runner:
```
docker model run hf.co/JGOS-Model/JGOS-31B-Citizen
```

openfree commited on 1 day ago

Commit

2dd0ff5

verified ·

1 Parent(s): 4459d8a

docs: K-AI #1 image + Darwin V8 evolution + Korean SFT overview

Browse files

Files changed (1) hide show

README.md +22 -4

README.md CHANGED Viewed

@@ -7,8 +7,30 @@ pipeline_tag: text-generation
 # JGOS-31B-Citizen
 전남·광주 통합특별시 시민을 위한 AI 어시스턴트입니다.
 ## Evaluation
 ### GPQA Diamond (198 questions)
@@ -16,7 +38,3 @@ pipeline_tag: text-generation
 | Method (test-time compute) | Score |
 |----------------------------|-------|
 | maj@8 + DELPHI + near-miss maj@32–64 (weighted vote) | **84.34%** (167/198) |
-This model is introduced in [Darwin Family](https://arxiv.org/abs/2605.14386).

 # JGOS-31B-Citizen
+<p align="center">
+  <img src="https://huggingface.co/JGOS-Model/JGOS-31B-Citizen/resolve/main/k-ai.png" alt="K-AI 리더보드 1위" width="780"/>
+</p>
+<p align="center">
+  🏆 <b>K-AI 리더보드 1위</b> · 대한민국 한국어 AI 종합 벤치마크 (<a href="https://leaderboard.aihub.or.kr/leaderboard">leaderboard.aihub.or.kr</a>)
+</p>
 전남·광주 통합특별시 시민을 위한 AI 어시스턴트입니다.
+## 모델 개요
+JGOS-31B-Citizen은 VIDRAFT의 **Darwin V8 플랫폼**으로 구축한 한국어 특화 대규모 언어모델입니다.
+- **모델 교배·진화 (Darwin V8)** — gemma4-31b 계열을 베이스로 하는 자사 Darwin 31B 모델군을 대상으로, Darwin V8 플랫폼의 모델 교배(merge)·진화(evolution) 파이프라인을 통해 **여러 세대에 걸쳐** 교배·선택·진화시키며 능력을 축적했습니다.
+- **한국어 특화 추가 학습** — 진화된 모델에 **한국어 특화 데이터셋**을 추가 학습하여 한국어 이해·추론 및 지역 행정 도메인 성능을 강화했습니다.
+> 진화 세대 구성, 교배 전략, 데이터셋 구성, 학습 설정 등 세부 사항은 영업기밀로 비공개입니다.
+## 주요 성과
+- 🏆 **K-AI 리더보드 1위** — 대한민국 한국어 AI 종합 벤치마크 (KMMLU-Pro · CLIcK · HLE · MuSR · Com2)
+- **GPQA Diamond 84.34%**
 ## Evaluation
 ### GPQA Diamond (198 questions)
 | Method (test-time compute) | Score |
 |----------------------------|-------|
 | maj@8 + DELPHI + near-miss maj@32–64 (weighted vote) | **84.34%** (167/198) |