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skt/A.X-4.0 · Hugging Face

🤗 Models | 💬 Chat | 📬 APIs (FREE!) | 🖥️ Github

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)

• transformers>=4.46.0 or the latest version is required to use skt/A.X-4.0

pip install transformers>=4.46.0

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)

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

pip install vllm>=v0.6.4.post1

VLLM_OPTION="--enable-auto-tool-choice --tool-call-parser hermes"
vllm serve skt/A.X-4.0 $VLLM_OPTION

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)



messages = [{"role": "user", "content": "What is the appropriate temperature for air conditioning in summer? Response in a single sentence."}]
call(messages, model)

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]


messages = [{"role": "user", "content": "우리가 뭘 사야되는데 원래 57600원인데 직원할인 받을 수 있거든? 할인가좀 계산해줘"}]
call(messages, model)





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)





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)

@article{SKTAdotX4,
  title={A.X 4.0},
  author={SKT AI Model Lab},
  year={2025},
  url={https://huggingface.co/skt/A.X-4.0}
}

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

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