Instructions to use Anserwise/AWAXIS-KR-31B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Anserwise/AWAXIS-KR-31B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Anserwise/AWAXIS-KR-31B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Anserwise/AWAXIS-KR-31B")
model = AutoModelForMultimodalLM.from_pretrained("Anserwise/AWAXIS-KR-31B", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Anserwise/AWAXIS-KR-31B with vLLM:

Install from pip and serve model

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

SGLang

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

Anserwise commited on Jun 8

Commit

e791557

verified ·

1 Parent(s): da92efe

feat: 학습 데이터셋 섹션 추가 (AI Hub 한국어 말뭉치)

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README.md +12 -0

README.md CHANGED Viewed

@@ -221,6 +221,18 @@ print(tok.decode(out[0][inp["input_ids"].shape[-1]:], skip_special_tokens=True))
 ---
 ## License
 This model includes **Gemma-4 lineage** weights and complies with the [Gemma Terms of Use](https://ai.google.dev/gemma/terms).

 ---
+## 학습 데이터셋
+본 모델은 한국지능정보사회진흥원(NIA)이 구축·배포하는 **AI Hub** 대용량 한국어 말뭉치로 학습되었습니다.
+- [대규모 구매도서 기반 한국어 말뭉치](https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=653)
+- [멀티세션·대화 관련 말뭉치 데이터](https://aihub.or.kr/aihubdata/data/view.do?currMenu=511&topMenu=100&aihubDataSe=dataPckage&dataPckageSn=1)
+- [한국어 대화 데이터셋](https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=272)
+- [대규모 웹데이터 기반 한국어 말뭉치](https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=624)
+- [의료·법률 전문 서적 말뭉치](https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=71487)
+- [전문 의학지식 데이터](https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=71874)
 ## License
 This model includes **Gemma-4 lineage** weights and complies with the [Gemma Terms of Use](https://ai.google.dev/gemma/terms).