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 / README.md

openfree

docs: reposition as administrative/public-sector AI (remove regional specificity)

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	---
	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> · 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.