A.X 3.1 Light
A.X 3.1 Light Highlights
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
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
🚀 Quickstart
with HuggingFace Transformers
transformers>=4.46.0or the latest version is required to useskt/A.X-3.1-Light
pip install transformers>=4.46.0
Example Usage
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.post1or the latest version is required to use tool-use feature
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
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
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