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
```

JGOS-31B-Citizen

File size: 4,392 Bytes

6b296ae
 
 
280bdc4
6b296ae
280bdc4
 
 
b60ef83
 
280bdc4
4cb832d
 
 
280bdc4
b60ef83
 
 
 
 
 
 
 
 
 
 
 
 
 
6b296ae
 
 
 
2dd0ff5
0911fc6
2dd0ff5
 
 
0911fc6
2dd0ff5
 
4cb832d
cd24ffb
0911fc6
2dd0ff5
0911fc6
2dd0ff5
0911fc6
4cb832d
2dd0ff5
0911fc6
280bdc4
0911fc6
280bdc4
0911fc6
 
 
 
 
 
4cb832d
2dd0ff5
0911fc6
2dd0ff5
0911fc6
 
2dd0ff5
cd24ffb
 
 
 
 
 
4cb832d
43e2d55
 
 
4cb832d
43e2d55
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4cb832d
280bdc4
 
0911fc6

---
language:
- ko
license: gemma
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- darwin
- darwin-v8
- gemma4
- korean
- administrative-ai
- public-sector
- government
- multimodal
- image-text-to-text
- reasoning
- thinking
- conversational
- gpqa
- benchmark
- leaderboard
- k-ai
- k-ai-leaderboard
- vidraft
- jgos
- text-generation
- ffn-transfer
- model-merge
---

# JGOS-31B-Citizen

<p align="center">
  <img src="https://huggingface.co/JGOS-Model/JGOS-31B-Citizen/resolve/main/k-ai.png" alt="#1 on the K-AI Leaderboard" width="780"/>
</p>

<p align="center">
  🏆 <b>#1 on the K-AI Leaderboard</b> &middot; Korea's national Korean-language AI benchmark (<a href="https://leaderboard.aihub.or.kr/leaderboard">leaderboard.aihub.or.kr</a>)
</p>

**JGOS-31B-Citizen** is a Korean, multimodal large language model **specialized for administrative & public-sector AI services** — civil-complaint response, public-document understanding, and government-domain question answering.

## Overview

JGOS-31B-Citizen is built on VIDRAFT's **Darwin V8** platform.

- **Base + FFN transfer, breeding & evolution (Darwin V8).** Starting from our in-house **gemma4-31b** base, the **feed-forward network (FFN)** blocks of multiple source models are extracted and grafted, then bred (merged) and evolved across **multiple generations** through the Darwin V8 pipeline to accumulate capability.
- **Korean administrative-domain fine-tuning.** The evolved model is further trained on **Korean-specialized datasets** to strengthen Korean comprehension, reasoning, and **administrative/public-sector domain** performance.

> The set of grafted source models, the number of evolution generations, the breeding strategy, dataset composition, and training configuration are proprietary and not disclosed.

## Specifications

| Item | Value |
|------|-------|
| Parameters | ~31B (dense) |
| Modality | Text + Image (multimodal) |
| Context length | up to 256K tokens |
| Base family | gemma4-31b (Gemma-compatible architecture) |
| Focus | Administrative & public-sector AI services |

## Highlights

- 🏆 **#1 on the K-AI Leaderboard** — Korea's national Korean-language AI benchmark (KMMLU-Pro · CLIcK · HLE · MuSR · Com2)
- **GPQA Diamond: 84.34%**

## Evaluation

### GPQA Diamond (198 questions)

| Method (test-time compute) | Score |
|----------------------------|-------|
| maj@8 + tie-retry + DELPHI + near-miss maj@32-64 (weighted vote) | **84.34%** (167/198) |

## Training Datasets

JGOS-31B-Citizen was trained using large-scale Korean corpora sourced from the **Korean AI Hub (AIHub)** — Korea's national AI data repository operated by NIA. The following datasets were used to optimize performance on the **K-AI Leaderboard** benchmarks (KoMMLU-Pro, CLIcK, HLE, MuSR, Com2):

| # | Dataset Name | AIHub Link |
|---|---|---|
| 1 | Medical and Legal Professional Books Corpus | [71487](https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=71487) |
| 2 | Financial and Legal Document Machine Reading Comprehension | [71610](https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=71610) |
| 3 | Large-scale Web-based Korean Corpus | [624](https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=624) |
| 4 | Large-scale Book-based Korean Corpus | [653](https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=653) |
| 5 | National Records Large-scale AI Learning Corpus | [71788](https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=71788) |
| 6 | Korean Generation-based Common Sense Reasoning Dataset | [459](https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=459) |
| 7 | Multi-session Dialogue Corpus | [pkg1](https://aihub.or.kr/aihubdata/data/view.do?currMenu=511&topMenu=100&aihubDataSe=dataPckage&dataPckageSn=1) |
| 8 | Essential Medical Knowledge Data (142GB) | [71875](https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=71875) |
| 9 | Specialized Medical Knowledge Data (206GB) | [71874](https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=71874) |
| 10 | Korean Dialogue Dataset | [272](https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=272) |

> All datasets are publicly available via [AIHub](https://aihub.or.kr) (registration required).

## License

This model is built on a Gemma-family architecture and is distributed under the [**Gemma Terms of Use**](https://ai.google.dev/gemma/terms). By using this model, you agree to the Gemma license terms.