Instructions to use youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension with Transformers:

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

pipe = pipeline("text-generation", model="youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension")
model = AutoModelForCausalLM.from_pretrained("youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension

SGLang

How to use youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension 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 "youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension" \
    --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": "youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension",
		"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 "youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension" \
        --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": "youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension with Docker Model Runner:
```
docker model run hf.co/youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension
```

A.X-3.1-Light SFT v3.2 AI Hub Extension

이 모델은 skt/A.X-3.1-Light를 기반으로 한국어 질의응답 및 지시 수행 데이터를 1 epoch LoRA SFT한 뒤 adapter를 base model에 병합한 BF16 전체 가중치 모델입니다. 답변형·객관식 문제에서 첫 문장 또는 첫 부분에 핵심 답을 제시하고, 필요한 경우 짧은 근거를 덧붙이도록 학습했습니다. 연구 및 평가용 모델이며, 생성 결과가 부정확할 수 있으므로 의료·법률·재정 등 고위험 의사결정의 유일한 근거로 사용해서는 안 됩니다.

Model information

Model name: A.X-3.1-Light SFT v3.2 AI Hub Extension
Base model: skt/A.X-3.1-Light
Base model revision: 9b41bb2406472634d8812c0b8931fa40fa9a6c3a
Fine-tuning method: LoRA supervised fine-tuning, merged for inference
Model format: standalone BF16 safetensors; no separate adapter is required
Intended use: Korean text generation, instruction following, and research evaluation

Training

Training data file: format_sft_answer_first_extension_13801.jsonl
Selected rows: 13,801
Serialized training examples: 13,793 (8 rows skipped during example construction)
Epochs: 1
Maximum sequence length: 2,048
Precision: BF16
Learning rate: 5e-5
Effective batch size: 8
Scheduler: linear; warmup steps: 0
Weight decay: 0
LoRA rank / alpha / dropout: 16 / 32 / 0.05
LoRA target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Objective: assistant-token causal language-model cross entropy
Training loss: 1.0445830774
Training runtime: 4,646.3862 seconds

The target contract is answer-first: a compact answer is given first, followed by at most one short rationale when a rationale is available. New AI Hub candidates were filtered to fit the base model with a 2,048-token maximum.

Training data

The final training mixture contains 5,801 examples inherited from the v0.21 answer-first core and 8,000 newly selected examples from three AI Hub sources. The source dataset IDs and selected counts are listed below. The source data, benchmark questions, answers, and evaluation artifacts are not included in this repository. Users must follow the applicable AI Hub terms of use.

AI Hub dataset	Description	Selected examples
569	행정 문서 대상 기계독해 데이터 / VL_multiple_choice	2,787
71610	금융·법률 문서 기계독해 데이터 / VL_4 다지선다	644
71857	국어 교과 지문형 문제 데이터	597
71874	전문 의학지식 데이터	823
71890	AI 파운데이션 모델 LLM/LAM 사후학습용 데이터	3,000
71894	지식·지능 데이터	3,000
71904	생각과정 씨앗 학습·검증 데이터	2,000
71949	인과관계 기반 추론 데이터(업사이클링) / label_json	950
Total		13,801

The new-data selection was deterministic with seed 20260831. It covered law, science/technology, mathematics, general knowledge, Korean culture/history, math, science, and social studies categories according to the selection manifest used for this run. Prompt-level duplicate checking found no duplicate prompts or replacements; inherited duplicate SFT IDs may remain as recorded in the manifest.

AI Hub references:

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

The bundled tokenizer includes the chat template used by the base model. For chat-style inference, use tokenizer.apply_chat_template and keep the prompt format consistent with the intended conversational interface.

Reproducibility

Local adapter checkpoint: /home/youngseok3/KDS/checkpoints/aihub_extension_20260831_gpu3
Local merged output: /home/youngseok3/KDS/submission/AX-3.1-Light-sft_v3_2_aihub_extension-merged
Dataset selection manifest: data/processed/aihub_extension_20260831/selection_manifest.json
Base model revision and merge details are recorded in kds_merge_info.json.

Limitations and license

This model is derived from the Apache-2.0 licensed skt/A.X-3.1-Light model. The base model's notices and SK Telecom trademark terms also apply. AI Hub dataset terms apply to the source data. See LICENSE and the base model repository for the applicable terms.

The model may produce incorrect, incomplete, biased, or poorly formatted answers. It has not been validated as a professional medical, legal, financial, or safety-critical system.

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension

Base model

skt/A.X-3.1-Light

Finetuned

(22)

this model