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--- |
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license: apache-2.0 |
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license_link: https://huggingface.co/skt/A.X-4.0-Light/blob/main/LICENSE |
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language: |
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- en |
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- ko |
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pipeline_tag: text-generation |
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library_name: transformers |
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model_id: skt/A.X-4.0-Light |
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developers: SKT AI Model Lab |
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model-index: |
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- name: A.X-4.0-Light |
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results: |
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- task: |
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type: generate_until |
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name: mmlu |
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dataset: |
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name: mmlu (chat CoT) |
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type: hails/mmlu_no_train |
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metrics: |
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- type: exact_match |
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value: 75.43 |
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name: exact_match |
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- task: |
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type: generate_until |
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name: kmmlu |
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dataset: |
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name: kmmlu (chat CoT) |
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type: HAERAE-HUB/KMMLU |
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metrics: |
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- type: exact_match |
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value: 64.15 |
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name: exact_match |
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--- |
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# A.X 4.0 Light |
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<p align="center"> |
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<picture> |
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<img src="./assets/A.X_logo_ko_4x3.png" width="45%" style="margin: 40px auto;"> |
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</picture> |
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</p> |
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<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> |
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## A.X 4.0 Family Highlights |
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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. |
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- **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). |
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- **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). |
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- **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. |
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- **Deployment Flexibility**: Offered in both a 72B-parameter standard model (A.X 4.0) and a 7B lightweight version (A.X 4.0 Light). |
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- **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) |
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## Performance |
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### Model Performance |
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<table><thead> |
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<tr> |
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<th colspan="2">Benchmarks</th> |
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<th>A.X 4.0</th> |
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<th>Qwen3-235B-A22B<br/>(w/o reasoning)</th> |
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<th>Qwen2.5-72B</th> |
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<th>GPT-4o</th> |
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</tr></thead> |
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<tbody> |
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<tr> |
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<td rowspan="4">Knowledge</td> |
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<td>KMMLU</td> |
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<td>78.32</td> |
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<td>73.64</td> |
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<td>66.44</td> |
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<td>72.51</td> |
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</tr> |
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<tr> |
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<td>CLIcK</td> |
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<td>83.51</td> |
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<td>74.55</td> |
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<td>72.59</td> |
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<td>80.22</td> |
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</tr> |
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<tr> |
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<td>KoBALT</td> |
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<td>47.30</td> |
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<td>41.57</td> |
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<td>37.00</td> |
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<td>44.00</td> |
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</tr> |
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<tr> |
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<td>MMLU</td> |
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<td>86.62</td> |
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<td>87.37</td> |
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<td>85.70</td> |
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<td>88.70</td> |
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</tr> |
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<tr> |
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<td rowspan="3">General</td> |
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<td>Ko-MT-Bench</td> |
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<td>86.69</td> |
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<td>88.00</td> |
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<td>82.69</td> |
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<td>88.44</td> |
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</tr> |
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<tr> |
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<td>MT-Bench</td> |
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<td>83.25</td> |
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<td>86.56</td> |
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<td>93.50</td> |
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<td>88.19</td> |
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</tr> |
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<tr> |
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<td>LiveBench<sup>2024.11</sup></td> |
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<td>52.30</td> |
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<td>64.50</td> |
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<td>54.20</td> |
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<td>52.19</td> |
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</tr> |
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<tr> |
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<td rowspan="2">Instruction Following</td> |
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<td>Ko-IFEval</td> |
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<td>77.96</td> |
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<td>77.53</td> |
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<td>77.07</td> |
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<td>75.38</td> |
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</tr> |
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<tr> |
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<td>IFEval</td> |
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<td>86.05</td> |
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<td>85.77</td> |
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<td>86.54</td> |
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<td>83.86</td> |
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</tr> |
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<tr> |
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<td rowspan="2">Math</td> |
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<td>HRM8K</td> |
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<td>48.55</td> |
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<td>54.52</td> |
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<td>46.37</td> |
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<td>43.27</td> |
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</tr> |
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<tr> |
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<td>MATH</td> |
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<td>74.28</td> |
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<td>72.72</td> |
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<td>77.00</td> |
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<td>72.38</td> |
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</tr> |
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<tr> |
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<td rowspan="3">Code</td> |
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<td>HumanEval+</td> |
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<td>79.27</td> |
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<td>79.27</td> |
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<td>81.71</td> |
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<td>86.00</td> |
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</tr> |
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<tr> |
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<td>MBPP+</td> |
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<td>73.28</td> |
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<td>70.11</td> |
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<td>75.66</td> |
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<td>75.10</td> |
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</tr> |
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<tr> |
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<td>LiveCodeBench<sup>2024.10~2025.04</sup></td> |
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<td>26.07</td> |
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<td>33.09</td> |
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<td>27.58</td> |
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<td>29.30</td> |
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</tr> |
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<tr> |
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<td>Long Context</td> |
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<td>LongBench<sup><128K</sup></td> |
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<td>56.70</td> |
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<td>49.40</td> |
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<td>45.60</td> |
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<td>47.50</td> |
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</tr> |
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<tr> |
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<td>Tool-use</td> |
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<td>FunctionChatBench</td> |
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<td>85.96</td> |
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<td>82.43</td> |
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<td>88.30</td> |
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<td>95.70</td> |
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</tr> |
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</tbody></table> |
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### Lightweight Model Performance |
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<table><thead> |
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<tr> |
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<th colspan="2">Benchmarks</th> |
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<th>A.X 4.0 Light</th> |
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<th>Qwen3-8B<br/>(w/o reasoning)</th> |
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<th>Qwen2.5-7B</th> |
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<th>EXAONE-3.5-7.8B</th> |
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<th>Kanana-1.5-8B</th> |
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</tr></thead> |
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<tbody> |
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<tr> |
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<td rowspan="4">Knowledge</td> |
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<td>KMMLU</td> |
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<td>64.15</td> |
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<td>63.53</td> |
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<td>49.56</td> |
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<td>53.76</td> |
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<td>48.28</td> |
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</tr> |
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<tr> |
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<td>CLIcK</td> |
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<td>68.05</td> |
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<td>62.71</td> |
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<td>60.56</td> |
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<td>64.30</td> |
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<td>61.30</td> |
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</tr> |
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<tr> |
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<td>KoBALT</td> |
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<td>30.29</td> |
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<td>26.57</td> |
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<td>21.57</td> |
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<td>21.71</td> |
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<td>23.14</td> |
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</tr> |
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<tr> |
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<td>MMLU</td> |
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<td>75.43</td> |
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<td>82.89</td> |
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<td>75.40</td> |
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<td>72.20</td> |
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<td>68.82</td> |
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</tr> |
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<tr> |
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<td rowspan="3">General</td> |
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<td>Ko-MT-Bench</td> |
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<td>79.50</td> |
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<td>64.06</td> |
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<td>61.31</td> |
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<td>81.06</td> |
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<td>76.30</td> |
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</tr> |
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<tr> |
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<td>MT-Bench</td> |
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<td>81.56</td> |
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<td>65.69</td> |
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<td>79.37</td> |
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<td>83.50</td> |
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<td>77.60</td> |
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</tr> |
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<tr> |
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<td>LiveBench</td> |
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<td>37.10</td> |
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<td>50.20</td> |
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<td>37.00</td> |
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<td>40.20</td> |
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<td>29.40</td> |
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</tr> |
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<tr> |
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<td rowspan="2">Instruction Following</td> |
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<td>Ko-IFEval</td> |
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<td>72.99</td> |
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<td>73.39</td> |
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<td>60.73</td> |
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<td>65.01</td> |
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<td>69.96</td> |
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</tr> |
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<tr> |
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<td>IFEval</td> |
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<td>84.68</td> |
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<td>85.38</td> |
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<td>76.73</td> |
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<td>82.61</td> |
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<td>80.11</td> |
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</tr> |
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<tr> |
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<td rowspan="2">Math</td> |
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<td>HRM8K</td> |
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<td>40.12</td> |
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<td>52.50</td> |
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<td>35.13</td> |
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<td>31.88</td> |
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<td>30.87</td> |
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</tr> |
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<tr> |
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<td>MATH</td> |
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<td>68.88</td> |
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<td>71.48</td> |
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<td>65.58</td> |
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<td>63.20</td> |
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<td>59.28</td> |
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</tr> |
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<tr> |
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<td rowspan="3">Code</td> |
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<td>HumanEval+</td> |
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<td>75.61</td> |
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<td>77.44</td> |
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<td>74.39</td> |
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<td>76.83</td> |
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<td>76.83</td> |
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</tr> |
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<tr> |
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<td>MBPP+</td> |
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<td>67.20</td> |
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<td>62.17</td> |
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<td>68.50</td> |
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<td>64.29</td> |
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<td>67.99</td> |
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</tr> |
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<tr> |
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<td>LiveCodeBench</td> |
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<td>18.03</td> |
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<td>23.93</td> |
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<td>16.62</td> |
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<td>17.98</td> |
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<td>16.52</td> |
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</tr> |
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</tbody></table> |
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## 🚀 Quickstart |
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### with HuggingFace Transformers |
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- `transformers>=4.46.0` or the latest version is required to use `skt/A.X-4.0-Light` |
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```bash |
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pip install transformers>=4.46.0 |
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``` |
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#### Example Usage |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "skt/A.X-4.0-Light" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype=torch.bfloat16, |
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device_map="auto", |
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) |
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model.eval() |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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messages = [ |
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{"role": "system", "content": "당신은 사용자가 제공하는 영어 문장들을 한국어로 번역하는 AI 전문가입니다."}, |
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{"role": "user", "content": "The first human went into space and orbited the Earth on April 12, 1961."}, |
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] |
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input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) |
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with torch.no_grad(): |
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output = model.generate( |
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input_ids, |
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max_new_tokens=128, |
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do_sample=False, |
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) |
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len_input_prompt = len(input_ids[0]) |
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response = tokenizer.decode(output[0][len_input_prompt:], skip_special_tokens=True) |
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print(response) |
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# Output: |
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# 1961년 4월 12일, 최초의 인간이 우주로 나가 지구를 공전했습니다. |
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``` |
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### with vLLM |
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- `vllm>=v0.6.4.post1` or the latest version is required to use tool-use function |
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```bash |
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pip install vllm>=v0.6.4.post1 |
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# if you don't want to activate tool-use function, just commenting out below vLLM option |
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VLLM_OPTION="--enable-auto-tool-choice --tool-call-parser hermes" |
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vllm serve skt/A.X-4.0-Light $VLLM_OPTION |
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``` |
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#### Example Usage |
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```python |
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from openai import OpenAI |
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def call(messages, model): |
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completion = client.chat.completions.create( |
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model=model, |
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messages=messages, |
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) |
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print(completion.choices[0].message) |
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client = OpenAI( |
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base_url="http://localhost:8000/v1", |
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api_key="api_key" |
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) |
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model = "skt/A.X-4.0-Light" |
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messages = [{"role": "user", "content": "에어컨 여름철 적정 온도는? 한줄로 답변해줘"}] |
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call(messages, model) |
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# Output: |
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# ChatCompletionMessage(content='여름철 적정 에어컨 온도는 일반적으로 24-26도입니다.', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[], reasoning_content=None) |
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messages = [{"role": "user", "content": "What is the appropriate temperature for air conditioning in summer? Response in a single sentence."}] |
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call(messages, model) |
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# Output: |
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# 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) |
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``` |
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#### Examples for tool-use |
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```python |
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from openai import OpenAI |
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def call(messages, model): |
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completion = client.chat.completions.create( |
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model=model, |
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messages=messages, |
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tools=tools |
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) |
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print(completion.choices[0].message) |
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client = OpenAI( |
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base_url="http://localhost:8000/v1", |
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api_key="api_key" |
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) |
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model = "skt/A.X-4.0-Light" |
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calculate_discount = { |
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"type": "function", |
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"function": { |
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"name": "calculate_discount", |
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"description": "원가격과 할인율(퍼센트 단위)을 입력받아 할인된 가격을계산한다.", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"original_price": { |
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"type": "number", |
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"description": "상품의 원래 가격" |
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}, |
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"discount_percentage": { |
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"type": "number", |
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"description": "적용할 할인율(예: 20% 할인의 경우 20을 입력)" |
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} |
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}, |
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"required": ["original_price", "discount_percentage"] |
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} |
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} |
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} |
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get_exchange_rate = { |
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|
"type": "function", |
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"function": { |
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"name": "get_exchange_rate", |
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|
"description": "두 통화 간의 환율을 가져온다.", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"base_currency": { |
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"type": "string", |
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"description": "The currency to convert from." |
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}, |
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"target_currency": { |
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"type": "string", |
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"description": "The currency to convert to." |
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} |
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}, |
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"required": ["base_currency", "target_currency"] |
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} |
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} |
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} |
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tools = [calculate_discount, get_exchange_rate] |
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|
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|
### Slot filling ### |
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|
messages = [{"role": "user", "content": "우리가 뭘 사야되는데 원래 57600원인데 직원할인 받을 수 있거든? 할인가좀 계산해줘"}] |
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call(messages, model) |
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# Output: |
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|
# 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: [[email protected]]([email protected]) |
