A.X 3.1 Light (pronounced "A dot X") is a light weight LLM optimized for Korean-language understanding and enterprise deployment. This sovereign AI model was developed entirely in-house by SKT, encompassing model architecture, data curation, and training, all carried out on SKT’s proprietary supercomputing infrastructure, TITAN. The model was trained from scratch on a high-quality multilingual corpus comprising 1.65 trillion tokens, with a primary focus on the Korean language. With a strong emphasis on data quality, A.X 3.1 Light achieves Pareto-optimal performance among Korean LLMs relative to its training corpus size, enabling highly efficient and cost-effective compute usage.

Authentic Korean Sovereign AI: A.X 3.1 Light was trained on a high-quality multilingual dataset—fully curated in-house—using SKT’s proprietary GPU infrastructure.
Highly Efficient Multilingual LLM: A.X 3.1 Light demonstrates superior performance among open-source Korean LLMs, despite its relatively compact training size of 1.65 trillion tokens.
Superior Korean Proficiency: A.X 3.1 Light achieved a score of 61.7 on the KMMLU: the leading benchmark for Korean-language evaluation and a Korean-specific adaptation of MMLU, outperforming other Korean-specified models.
Deep Korean Understanding: A.X 3.1 Light obtained 27.43 on the KoBALT-700: a benchmark for Korean advanced linguistic tasks, outperforming other Korean-specialized models.
Efficient Token Usage: A.X 3.1 Light requires approximately 33% fewer tokens than GPT-4o to process equivalent Korean inputs, facilitating more cost-effective and computationally efficient inference.
Long-Context Handling: A.X 3.1 Light supports up to 32,768 tokens.

Core Technologies

A.X 3.1 Light represents an efficient sovereign AI model, developed end-to-end by SKT, encompassing model architecture, data curation, infrastructure deployment, and optimization.

Model Architecture Specs

Model	# Params	# Layers	# KV-Heads	Hidden Dim	FFN Dim
A.X 3.1 Light	7B	32	32	4096	10880

High-Quality Data Pipeline & Strategic Mixture

We collected and curated a training dataset comprising 20 trillion tokens sourced from diverse domains.
The entire dataset was processed through SKT’s proprietary data pipeline, incorporating synthetic data generation and comprehensive quality filtering.
For training A.X 3.1 Light, a total of 1.65 trillion tokens were utilized, comprising a Korean-focused multilingual corpus.

Pareto-Optimal Compute Efficiency

A.X 3.1 Light achieves 5 to 6 times lower computational cost compared to models with similar performance levels. Rigorous data curation and two-stage training with STEM-focused data enabled competitive performance at reduced FLOPs.

Benchmark Results

Benchmarks	A.X 3.1 Light	Kanana-1.5-8B	EXAONE-3.5-7.8B	Qwen2.5-7B	Qwen3-8B (w/o reasoning)
Knowledge	KMMLU	61.70	48.28	53.76	49.56	63.53
KMMLU-pro	45.54	37.63	40.11	38.87	50.71
KMMLU-redux	52.34	35.33	42.21	38.58	55.74
CLIcK	71.22	61.30	64.11	58.30	63.31
KoBALT	27.43	23.14	21.71	21.57	26.57
MMLU	66.95	68.82	72.20	75.40	82.89
General	Ko-MT-Bench	78.56	76.30	81.06	61.31	64.06
MT-Bench	74.38	77.60	83.50	79.37	65.69
Instruction Following	Ko-IFEval	70.04	69.96	65.01	60.73	73.39
IFEval	79.86	80.11	82.61	76.73	85.38
Math	HRM8K	41.70	30.87	31.88	35.13	52.50
MATH	70.14	59.28	63.20	65.58	71.48
Code	HumanEval+	73.78	76.83	76.83	74.39	77.44
MBPP+	61.64	67.99	64.29	68.50	62.17

🚀 Quickstart

with HuggingFace Transformers

transformers>=4.46.0 or the latest version is required to use skt/A.X-3.1-Light

pip install transformers>=4.46.0

Example Usage

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "skt/A.X-3.1-Light"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "당신은 사용자가 제공하는 영어 문장들을 한국어로 번역하는 AI 전문가입니다."},
    {"role": "user", "content": "The first human went into space and orbited the Earth on April 12, 1961."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(
        input_ids,
        max_new_tokens=128,
        do_sample=False,
    )

len_input_prompt = len(input_ids[0])
response = tokenizer.decode(output[0][len_input_prompt:], skip_special_tokens=True)
print(response)
# Output:
# 1961년 4월 12일, 최초의 인간이 우주에 나가 지구를 궤도를 돌았습니다.

with vLLM

vllm>=v0.6.4.post1 or the latest version is required to use tool-use feature

bash

pip install vllm>=v0.6.4.post1
# if you don't want to activate tool-use feature, just commenting out below vLLM option
VLLM_OPTION="--enable-auto-tool-choice --tool-call-parser hermes"
vllm serve skt/A.X-3.1-Light $VLLM_OPTION

Example Usage

python

from openai import OpenAI

def call(messages, model):
    completion = client.chat.completions.create(
        model=model,
        messages=messages,
    )
    print(completion.choices[0].message)

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="api_key"
)
model = "skt/A.X-3.1-Light"
messages = [{"role": "user", "content": "에어컨 여름철 적정 온도는? 한줄로 답변해줘"}]
call(messages, model)
# Output:
# 에어컨 여름철 적정 온도는 24~26도입니다.

messages = [{"role": "user", "content": "What is the appropriate temperature for air conditioning in summer? Respond in a single sentence."}]
call(messages, model)
# Output:
# The appropriate temperature for air conditioning in summer is generally set between 24 to 26°C for optimal comfort and energy efficiency.

Examples for tool-use

python

from openai import OpenAI


def call(messages, model):
    completion = client.chat.completions.create(
        model=model,
        messages=messages,
        tools=tools
    )
    print(completion.choices[0].message)


client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="api_key"
)
model = "skt/A.X-3.1-Light"

calculate_discount = {
    "type": "function",
    "function": {
        "name": "calculate_discount",
        "description": "원가격과 할인율(퍼센트 단위)을 입력받아 할인된 가격을계산한다.",
        "parameters": {
            "type": "object",
            "properties": {
                "original_price": {
                    "type": "number",
                    "description": "상품의 원래 가격"
                },
                "discount_percentage": {
                    "type": "number",
                    "description": "적용할 할인율"
                }
            },
            "required": ["original_price", "discount_percentage"]
        }
    }
}
get_exchange_rate = {
    "type": "function",
    "function": {
        "name": "get_exchange_rate",
        "description": "두 통화 간의 환율을 가져온다.",
        "parameters": {
            "type": "object",
            "properties": {
                "base_currency": {
                    "type": "string",
                    "description": "The currency to convert from."
                },
                "target_currency": {
                    "type": "string",
                    "description": "The currency to convert to."
                }
            },
            "required": ["base_currency", "target_currency"]
        }
    }
}
tools = [calculate_discount, get_exchange_rate]

### Slot filling ###
messages = [{"role": "user", "content": "우리가 뭘 사야되는데 원가가 57600원인데 직원할인 받으면 얼마야?"}]
call(messages, model)
# Output:
# ChatCompletionMessage(content='직원 할인을 적용하기 위해서는 할인율을 알 수 있어야 합니다. 할인율을 알려주실 수 있나요?', role='assistant', function_call=None, tool_calls=[], reasoning_content=None)


### Function calling ###
messages = [
    {"role": "user", "content": "우리가 뭘 사야되는데 원가가 57600원인데 직원할인 받으면 얼마야?"},
    {"role": "assistant", "content": "직원 할인을 적용하기 위해서는 할인율을 알 수 있어야 합니다. 할인율을 알려주실 수 있나요?"},
    {"role": "user", "content": "15% 할인 받을 수 있어."},
]
call(messages, model)
# Output: 
# ChatCompletionMessage(content=None, role='assistant', function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='chatcmpl-tool-3ebf11847364450daf363039db80cc50', function=Function(arguments='{"original_price": 57600, "discount_percentage": 15}', name='calculate_discount'), type='function')], reasoning_content=None)


### Completion ###
messages = [
    {"role": "user", "content": "우리가 뭘 사야되는데 원가가 57600원인데 직원할인 받으면 얼마야?"},
    {"role": "assistant", "content": ""},
    {"role": "user", "content": "15% 할인 받을 수 있어."},
    {"role": "tool", "tool_call_id": "random_id", "name": "calculate_discount", "content": "{\"original_price\": 57600, \"discount_percentage\": 15, \"discounted_price\": 48960.0}"}
]
call(messages, model)
# Output: 
# ChatCompletionMessage(content='57,600원의 상품에 15% 할인을 적용하면, 할인된 가격은 48,960원입니다.', role='assistant', function_call=None, tool_calls=[], reasoning_content=None)

License

The A.X 3.1 Light model is licensed under Apache License 2.0.

Citation

@article{SKTAdotX3.1Light,
  title={A.X 3.1 Light},
  author={SKT AI Model Lab},
  year={2025},
  url={https://huggingface.co/skt/A.X-3.1-Light}
}

Contact

Business & Partnership Contact: a.x@sk.com