| --- |
| 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 |
| --- |
| |
| # A.X 4.0 Light |
|
|
| <p align="center"> |
| <picture> |
| <img src="./assets/A.X_logo_ko_4x3.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><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) |
