Instructions to use jwg0830/AX-3.1-Light-sft_v0_21_prefill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jwg0830/AX-3.1-Light-sft_v0_21_prefill with Transformers:

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

pipe = pipeline("text-generation", model="jwg0830/AX-3.1-Light-sft_v0_21_prefill")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jwg0830/AX-3.1-Light-sft_v0_21_prefill")
model = AutoModelForCausalLM.from_pretrained("jwg0830/AX-3.1-Light-sft_v0_21_prefill", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jwg0830/AX-3.1-Light-sft_v0_21_prefill with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jwg0830/AX-3.1-Light-sft_v0_21_prefill"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jwg0830/AX-3.1-Light-sft_v0_21_prefill",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jwg0830/AX-3.1-Light-sft_v0_21_prefill

SGLang

How to use jwg0830/AX-3.1-Light-sft_v0_21_prefill 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 "jwg0830/AX-3.1-Light-sft_v0_21_prefill" \
    --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": "jwg0830/AX-3.1-Light-sft_v0_21_prefill",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "jwg0830/AX-3.1-Light-sft_v0_21_prefill" \
        --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": "jwg0830/AX-3.1-Light-sft_v0_21_prefill",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jwg0830/AX-3.1-Light-sft_v0_21_prefill with Docker Model Runner:
```
docker model run hf.co/jwg0830/AX-3.1-Light-sft_v0_21_prefill
```

AX-3.1-Light-sft_v0_21_prefill (동전, 프로토콜 프로브)

jwg0830/AX-3.1-Light-sft_v0_21(K-AI 실제 평균 0.4326)와 가중치가 완전히 동일한 모델입니다. 유일한 차이는 chat_template.jinja의 assistant 턴 시작이 <|assistant|> 대신 <|assistant|>정답: 으로 프리필돼 있다는 것뿐입니다.

목적: K-AI 채점이 출력 형식에 민감한지 실측하는 프로브. 가중치가 같으므로 점수 차이가 나면 100% 템플릿(형식) 효과입니다.
기대: 모델이 이미 "정답 우선" 스키마로 학습됐으므로 내용 자체는 거의 동일하게 생성되지만, 모든 응답이 강제로 정답: X로 시작합니다.

사용 데이터셋 (v0.21과 동일)

ID	데이터셋명	URL
71857	국어 교과 지문형 문제 데이터	https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=71857
71874	전문 의학지식 데이터	https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=71874
71610	금융, 법률 문서 기계독해 데이터	https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=71610
569	행정 문서 대상 기계독해 데이터	https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=569
71949	인과관계 기반 추론 데이터(업사이클링)	https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=71949

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for jwg0830/AX-3.1-Light-sft_v0_21_prefill

Base model

skt/A.X-3.1-Light

Finetuned

(25)

this model