A.X 4.0
A.X 4.0 Family Highlights
SK Telecom released A.X 4.0 (pronounced "A dot X"), a large language model (LLM) optimized for Korean-language understanding and enterprise deployment, on July 03, 2025. Built on the open-source Qwen2.5 model, A.X 4.0 has been further trained with large-scale Korean datasets to deliver outstanding performance in real-world business environments.
- Superior Korean Proficiency: Achieved a score of 78.3 on KMMLU, the leading benchmark for Korean-language evaluation and a Korean-specific adaptation of MMLU, outperforming GPT-4o (72.5).
- Deep Cultural Understanding: Scored 83.5 on CLIcK, a benchmark for Korean cultural and contextual comprehension, surpassing GPT-4o (80.2).
- Efficient Token Usage: A.X 4.0 uses approximately 33% fewer tokens than GPT-4o for the same Korean input, enabling more cost-effective and efficient processing.
- Deployment Flexibility: Offered in both a 72B-parameter standard model (A.X 4.0) and a 7B lightweight version (A.X 4.0 Light).
- Long Context Handling: Supports up to 131,072 tokens, allowing comprehension of lengthy documents and conversations. (Lightweight model supports up to 16,384 tokens length)
Performance
Model Performance
Lightweight Model Performance
🚀 Quickstart
with HuggingFace Transformers
transformers>=4.46.0or the latest version is required to useskt/A.X-4.0
pip install transformers>=4.46.0
Example Usage
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "skt/A.X-4.0"
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:
# 최초의 인간이 1961년 4월 12일에 우주로 가서 지구 궤도를 돌았습니다.
with vLLM
vllm>=v0.6.4.post1or the latest version is required to use tool-use function
pip install vllm>=v0.6.4.post1
# if you don't want to activate tool-use function, just commenting out below vLLM option
VLLM_OPTION="--enable-auto-tool-choice --tool-call-parser hermes"
vllm serve skt/A.X-4.0 $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-4.0"
messages = [{"role": "user", "content": "에어컨 여름철 적정 온도는? 한줄로 답변해줘"}]
call(messages, model)
# Output:
# ChatCompletionMessage(content='여름철 에어컨 적정 온도는 24~26도입니다.', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[], reasoning_content=None)
messages = [{"role": "user", "content": "What is the appropriate temperature for air conditioning in summer? Response in a single sentence."}]
call(messages, model)
# Output:
# ChatCompletionMessage(content='A comfortable and energy-efficient temperature for air conditioning in summer is typically around 24-26°C (75-79°F).', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[], reasoning_content=None)
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-4.0"
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(예: 20% 할인의 경우 20을 입력)"
}
},
"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 원래 57600원인데 직원할인 받을 수 있거든? 할인가좀 계산해줘"}]
call(messages, model)
# Output:
# ChatCompletionMessage(content='직원 할인을 몇 퍼센트 받을 수 있는지 알려주시겠어요?', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[], reasoning_content=None)
### Function calling ###
messages = [
{"role": "user", "content": "우리가 뭘 사야되는데 원래 57600원인데 직원할인 받을 수 있거든? 할인가좀 계산해줘"},
{"role": "assistant", "content": "직원 할인을 몇 퍼센트 받을 수 있는지 알려주시겠어요?"},
{"role": "user", "content": "15% 할인 받을 수 있어."},
]
call(messages, model)
# Output:
# ChatCompletionMessage(content=None, refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='chatcmpl-tool-8634f423e5494ebfa7a428b8385b4981', 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 원래 57600원인데 직원할인 받을 수 있거든? 할인가좀 계산해줘"},
{"role": "assistant", "content": "직원 할인을 몇 퍼센트 받을 수 있는지 알려주시겠어요?"},
{"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='15% 할인을 적용하면 상품의 가격은 48,960원이 됩니다.', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[], reasoning_content=None)
Citation
@article{SKTAdotX4,
title={A.X 4.0},
author={SKT AI Model Lab},
year={2025},
url={https://huggingface.co/skt/A.X-4.0}
}
Contact
- Business & Partnership Contact: a.x@sk.com