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

43e2d55

verified ·

1 Parent(s): 0911fc6

feat: add Training Datasets section (AIHub K-AI optimized)

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README.md +19 -0

README.md CHANGED Viewed

@@ -70,6 +70,25 @@ JGOS-31B-Citizen is built on VIDRAFT's **Darwin V8** platform.
 |----------------------------|-------|
 | maj@8 + tie-retry + DELPHI + near-miss maj@32–64 (weighted vote) | **84.34%** (167/198) |
 ## 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.

 |----------------------------|-------|
 | 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 (National Intelligence Agency for IT). 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.