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---
license: apache-2.0
license_link: https://huggingface.co/skt/A.X-4.0-Light/blob/main/LICENSE
language:
- en
- ko
pipeline_tag: text-generation
library_name: transformers
model_id: skt/A.X-4.0-Light
developers: SKT AI Model Lab
model-index:
- name: A.X-4.0-Light
results:
- task:
type: generate_until
name: mmlu
dataset:
name: mmlu (chat CoT)
type: hails/mmlu_no_train
metrics:
- type: exact_match
value: 75.43
name: exact_match
- task:
type: generate_until
name: kmmlu
dataset:
name: kmmlu (chat CoT)
type: HAERAE-HUB/KMMLU
metrics:
- type: exact_match
value: 64.15
name: exact_match
base_model:
- skt/A.X-4.0-Light
tags:
- A.X
---
# A.X 4.0 Light
<p align="center">
<picture>
<img src="https://i.imgur.com/RdZhPeZ.png" width="45%" style="margin: 40px auto;">
</picture>
</p>
<p align="center"> <a href="https://huggingface.co/collections/skt/ax-4-68637ebaa63b9cc51925e886">🤗 Models</a> | <a href="https://sktax.chat/chat">💬 Chat</a> | <a href="https://github.com/SKT-AI/A.X-4.0/blob/main/apis/README.md">📬 APIs (FREE!)</a> | <a href="https://github.com/SKT-AI/A.X-4.0">🖥️ Github</a> </p>
## 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](https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e) 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](https://huggingface.co/datasets/HAERAE-HUB/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](https://huggingface.co/datasets/EunsuKim/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
<table><thead>
<tr>
<th colspan="2">Benchmarks</th>
<th>A.X 4.0</th>
<th>Qwen3-235B-A22B<br/>(w/o reasoning)</th>
<th>Qwen2.5-72B</th>
<th>GPT-4o</th>
</tr></thead>
<tbody>
<tr>
<td rowspan="4">Knowledge</td>
<td>KMMLU</td>
<td>78.32</td>
<td>73.64</td>
<td>66.44</td>
<td>72.51</td>
</tr>
<tr>
<td>CLIcK</td>
<td>83.51</td>
<td>74.55</td>
<td>72.59</td>
<td>80.22</td>
</tr>
<tr>
<td>KoBALT</td>
<td>47.30</td>
<td>41.57</td>
<td>37.00</td>
<td>44.00</td>
</tr>
<tr>
<td>MMLU</td>
<td>86.62</td>
<td>87.37</td>
<td>85.70</td>
<td>88.70</td>
</tr>
<tr>
<td rowspan="3">General</td>
<td>Ko-MT-Bench</td>
<td>86.69</td>
<td>88.00</td>
<td>82.69</td>
<td>88.44</td>
</tr>
<tr>
<td>MT-Bench</td>
<td>83.25</td>
<td>86.56</td>
<td>93.50</td>
<td>88.19</td>
</tr>
<tr>
<td>LiveBench<sup>2024.11</sup></td>
<td>52.30</td>
<td>64.50</td>
<td>54.20</td>
<td>52.19</td>
</tr>
<tr>
<td rowspan="2">Instruction Following</td>
<td>Ko-IFEval</td>
<td>77.96</td>
<td>77.53</td>
<td>77.07</td>
<td>75.38</td>
</tr>
<tr>
<td>IFEval</td>
<td>86.05</td>
<td>85.77</td>
<td>86.54</td>
<td>83.86</td>
</tr>
<tr>
<td rowspan="2">Math</td>
<td>HRM8K</td>
<td>48.55</td>
<td>54.52</td>
<td>46.37</td>
<td>43.27</td>
</tr>
<tr>
<td>MATH</td>
<td>74.28</td>
<td>72.72</td>
<td>77.00</td>
<td>72.38</td>
</tr>
<tr>
<td rowspan="3">Code</td>
<td>HumanEval+</td>
<td>79.27</td>
<td>79.27</td>
<td>81.71</td>
<td>86.00</td>
</tr>
<tr>
<td>MBPP+</td>
<td>73.28</td>
<td>70.11</td>
<td>75.66</td>
<td>75.10</td>
</tr>
<tr>
<td>LiveCodeBench<sup>2024.10~2025.04</sup></td>
<td>26.07</td>
<td>33.09</td>
<td>27.58</td>
<td>29.30</td>
</tr>
<tr>
<td>Long Context</td>
<td>LongBench<sup>&lt;128K</sup></td>
<td>56.70</td>
<td>49.40</td>
<td>45.60</td>
<td>47.50</td>
</tr>
<tr>
<td>Tool-use</td>
<td>FunctionChatBench</td>
<td>85.96</td>
<td>82.43</td>
<td>88.30</td>
<td>95.70</td>
</tr>
</tbody></table>
### Lightweight Model Performance
<table><thead>
<tr>
<th colspan="2">Benchmarks</th>
<th>A.X 4.0 Light</th>
<th>Qwen3-8B<br/>(w/o reasoning)</th>
<th>Qwen2.5-7B</th>
<th>EXAONE-3.5-7.8B</th>
<th>Kanana-1.5-8B</th>
</tr></thead>
<tbody>
<tr>
<td rowspan="4">Knowledge</td>
<td>KMMLU</td>
<td>64.15</td>
<td>63.53</td>
<td>49.56</td>
<td>53.76</td>
<td>48.28</td>
</tr>
<tr>
<td>CLIcK</td>
<td>68.05</td>
<td>62.71</td>
<td>60.56</td>
<td>64.30</td>
<td>61.30</td>
</tr>
<tr>
<td>KoBALT</td>
<td>30.29</td>
<td>26.57</td>
<td>21.57</td>
<td>21.71</td>
<td>23.14</td>
</tr>
<tr>
<td>MMLU</td>
<td>75.43</td>
<td>82.89</td>
<td>75.40</td>
<td>72.20</td>
<td>68.82</td>
</tr>
<tr>
<td rowspan="3">General</td>
<td>Ko-MT-Bench</td>
<td>79.50</td>
<td>64.06</td>
<td>61.31</td>
<td>81.06</td>
<td>76.30</td>
</tr>
<tr>
<td>MT-Bench</td>
<td>81.56</td>
<td>65.69</td>
<td>79.37</td>
<td>83.50</td>
<td>77.60</td>
</tr>
<tr>
<td>LiveBench</td>
<td>37.10</td>
<td>50.20</td>
<td>37.00</td>
<td>40.20</td>
<td>29.40</td>
</tr>
<tr>
<td rowspan="2">Instruction Following</td>
<td>Ko-IFEval</td>
<td>72.99</td>
<td>73.39</td>
<td>60.73</td>
<td>65.01</td>
<td>69.96</td>
</tr>
<tr>
<td>IFEval</td>
<td>84.68</td>
<td>85.38</td>
<td>76.73</td>
<td>82.61</td>
<td>80.11</td>
</tr>
<tr>
<td rowspan="2">Math</td>
<td>HRM8K</td>
<td>40.12</td>
<td>52.50</td>
<td>35.13</td>
<td>31.88</td>
<td>30.87</td>
</tr>
<tr>
<td>MATH</td>
<td>68.88</td>
<td>71.48</td>
<td>65.58</td>
<td>63.20</td>
<td>59.28</td>
</tr>
<tr>
<td rowspan="3">Code</td>
<td>HumanEval+</td>
<td>75.61</td>
<td>77.44</td>
<td>74.39</td>
<td>76.83</td>
<td>76.83</td>
</tr>
<tr>
<td>MBPP+</td>
<td>67.20</td>
<td>62.17</td>
<td>68.50</td>
<td>64.29</td>
<td>67.99</td>
</tr>
<tr>
<td>LiveCodeBench</td>
<td>18.03</td>
<td>23.93</td>
<td>16.62</td>
<td>17.98</td>
<td>16.52</td>
</tr>
</tbody></table>
## 🚀 Quickstart
### with HuggingFace Transformers
- `transformers>=4.46.0` or the latest version is required to use `skt/A.X-4.0-Light`
```bash
pip install transformers>=4.46.0
```
#### Example Usage
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "skt/A.X-4.0-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 우주로 나가 지구를 공전했습니다.
```
### with vLLM
- `vllm>=v0.6.4.post1` or the latest version is required to use tool-use function
```bash
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-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-4.0-Light"
messages = [{"role": "user", "content": "에어컨 여름철 적정 온도는? 한줄로 답변해줘"}]
call(messages, model)
# Output:
# ChatCompletionMessage(content='여름철 적정 에어컨 온도는 일반적으로 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='The appropriate temperature for air conditioning in summer generally ranges from 72°F to 78°F (22°C to 26°C) for comfort and energy efficiency.', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[], reasoning_content=None)
```
#### 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-4.0-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(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='할인율을 알려주시겠습니까?', 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": "할인율을 알려주시겠습니까?"},
{"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-7778d1d9fca94bf2acbb44c79359502c', 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": "할인율을 알려주시겠습니까?"},
{"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='57600원의 상품에서 15% 할인을 적용하면, 할인된 가격은 48960원입니다.', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[], reasoning_content=None)
```
## License
The `A.X 4.0 Light` models are licensed under `Apache License 2.0`.
## Citation
```
@article{SKTAdotX4Light,
title={A.X 4.0 Light},
author={SKT AI Model Lab},
year={2025},
url={https://huggingface.co/skt/A.X-4.0-Light}
}
```
## Contact
- Business & Partnership Contact: [a.x@sk.com](a.x@sk.com)

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