423 lines
13 KiB
Markdown
423 lines
13 KiB
Markdown
---
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license: apache-2.0
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license_link: https://huggingface.co/skt/A.X-3.1-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-3.1-Light
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developers: SKT AI Model Lab
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model-index:
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- name: A.X-3.1-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: 66.95
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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: 61.70
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name: exact_match
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---
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# A.X 3.1 Light
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<div align="center">
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<img src="./assets/A.X_from_scratch_logo_ko_4x3.png" alt="A.X Logo" width="300"/>
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</div>
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<p align="center"> <a href="https://huggingface.co/collections/skt/ax-3-686b288b3b05e1234f3f4c73">🤗 Models</a> | <a href="https://github.com/SKT-AI/A.X-3">🖥️ Github</a> </p>
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## A.X 3.1 Light Highlights
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<!-- SK Telecom released **A.X 3.1 Light** (pronounced "A dot X"), a large language model (LLM) optimized for Korean-language understanding and enterprise deployment, on July 10, 2025. -->
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**A.X 3.1 Light** (pronounced "A dot X") is a light weight LLM optimized for Korean-language understanding and enterprise deployment.
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This sovereign AI model was developed entirely in-house by SKT, encompassing model architecture, data curation, and training, all carried out on SKT’s proprietary supercomputing infrastructure, TITAN.
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The model was trained from scratch on a high-quality multilingual corpus comprising **1.65 trillion tokens**, with a primary focus on the Korean language.
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With a strong emphasis on data quality, A.X 3.1 Light achieves **Pareto-optimal performance among Korean LLMs relative to its training corpus size**, enabling **highly efficient and cost-effective compute usage**.
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- **Authentic Korean Sovereign AI**: A.X 3.1 Light was trained on a high-quality multilingual dataset—fully curated in-house—using SKT’s proprietary GPU infrastructure.
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- **Highly Efficient Multilingual LLM**: A.X 3.1 Light demonstrates superior performance among open-source Korean LLMs, despite its relatively compact training size of 1.65 trillion tokens.
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- **Superior Korean Proficiency**: A.X 3.1 Light achieved a score of **61.7** on the [KMMLU](https://huggingface.co/datasets/HAERAE-HUB/KMMLU): the leading benchmark for Korean-language evaluation and a Korean-specific adaptation of MMLU, outperforming other Korean-specified models.
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- **Deep Korean Understanding**: A.X 3.1 Light obtained **27.43** on the [KoBALT-700](https://huggingface.co/datasets/snunlp/KoBALT-700): a benchmark for Korean advanced linguistic tasks, outperforming other Korean-specialized models.
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- **Efficient Token Usage**: A.X 3.1 Light requires approximately 33% fewer tokens than GPT-4o to process equivalent Korean inputs, facilitating more cost-effective and computationally efficient inference.
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- **Long-Context Handling**: A.X 3.1 Light supports up to **32,768 tokens**.
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## Core Technologies
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A.X 3.1 Light represents **an efficient sovereign AI model**, developed end-to-end by SKT, encompassing model architecture, data curation, infrastructure deployment, and optimization.
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### Model Architecture Specs
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<table><thead>
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<tr>
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<th>Model</th>
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<th># Params</th>
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<th># Layers</th>
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<th># KV-Heads</th>
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<th>Hidden Dim</th>
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<th>FFN Dim</th>
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</tr>
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<tr>
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<th>A.X 3.1 Light</th>
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<th>7B</th>
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<th>32</th>
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<th>32</th>
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<th>4096</th>
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<th>10880</th>
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</tr>
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</thead>
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</table>
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### High-Quality Data Pipeline & Strategic Mixture
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- We collected and curated a training dataset comprising 20 trillion tokens sourced from diverse domains.
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- The entire dataset was processed through SKT’s proprietary data pipeline, incorporating synthetic data generation and comprehensive quality filtering.
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- For training A.X 3.1 Light, a total of **1.65 trillion tokens** were utilized, comprising a Korean-focused multilingual corpus.
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### Pareto-Optimal Compute Efficiency
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A.X 3.1 Light achieves 5 to 6 times lower computational cost compared to models with similar performance levels.
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Rigorous data curation and two-stage training with STEM-focused data enabled competitive performance at reduced FLOPs.
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## Benchmark Results
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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 3.1 Light</th>
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<th>Kanana-1.5-8B</th>
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<th>EXAONE-3.5-7.8B</th>
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<th>Qwen2.5-7B</th>
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<th>Qwen3-8B<br>(w/o reasoning)</th>
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</tr></thead>
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<tbody>
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<tr>
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<td rowspan="6">Knowledge</td>
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<td>KMMLU</td>
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<td>61.70</td>
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<td>48.28</td>
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<td>53.76</td>
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<td>49.56</td>
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<td>63.53</td>
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</tr>
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<tr>
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<td>KMMLU-pro</td>
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<td>45.54</td>
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<td>37.63</td>
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<td>40.11</td>
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<td>38.87</td>
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<td>50.71</td>
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</tr>
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<tr>
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<td>KMMLU-redux</td>
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<td>52.34</td>
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<td>35.33</td>
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<td>42.21</td>
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<td>38.58</td>
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<td>55.74</td>
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</tr>
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<tr>
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<td>CLIcK</td>
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<td>71.22</td>
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<td>61.30</td>
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<td>64.11</td>
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<td>58.30</td>
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<td>63.31</td>
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</tr>
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<tr>
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<td>KoBALT</td>
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<td>27.43</td>
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<td>23.14</td>
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<td>21.71</td>
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<td>21.57</td>
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<td>26.57</td>
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</tr>
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<tr>
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<td>MMLU</td>
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<td>66.95</td>
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<td>68.82</td>
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<td>72.20</td>
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<td>75.40</td>
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<td>82.89</td>
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</tr>
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<tr>
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<td rowspan="2">General</td>
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<td>Ko-MT-Bench</td>
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<td>78.56</td>
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<td>76.30</td>
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<td>81.06</td>
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<td>61.31</td>
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<td>64.06</td>
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</tr>
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<tr>
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<td>MT-Bench</td>
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<td>74.38</td>
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<td>77.60</td>
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<td>83.50</td>
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<td>79.37</td>
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<td>65.69</td>
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</tr>
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<tr>
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<td rowspan="2">Instruction<br>Following</td>
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<td>Ko-IFEval</td>
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<td>70.04</td>
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<td>69.96</td>
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<td>65.01</td>
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<td>60.73</td>
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<td>73.39</td>
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</tr>
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<tr>
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<td>IFEval</td>
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<td>79.86</td>
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<td>80.11</td>
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<td>82.61</td>
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<td>76.73</td>
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<td>85.38</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>41.70</td>
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<td>30.87</td>
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<td>31.88</td>
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<td>35.13</td>
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<td>52.50</td>
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</tr>
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<tr>
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<td>MATH</td>
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<td>70.14</td>
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<td>59.28</td>
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<td>63.20</td>
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<td>65.58</td>
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<td>71.48</td>
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</tr>
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<tr>
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<td rowspan="2">Code<br></td>
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<td>HumanEval+</td>
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<td>73.78</td>
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<td>76.83</td>
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<td>76.83</td>
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<td>74.39</td>
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<td>77.44</td>
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</tr>
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<tr>
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<td>MBPP+</td>
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<td>61.64</td>
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<td>67.99</td>
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<td>64.29</td>
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<td>68.50</td>
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<td>62.17</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-3.1-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-3.1-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 우주에 나가 지구를 궤도를 돌았습니다.
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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 feature
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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 feature, 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-3.1-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-3.1-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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# 에어컨 여름철 적정 온도는 24~26도입니다.
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messages = [{"role": "user", "content": "What is the appropriate temperature for air conditioning in summer? Respond in a single sentence."}]
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call(messages, model)
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# Output:
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# The appropriate temperature for air conditioning in summer is generally set between 24 to 26°C for optimal comfort and energy efficiency.
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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-3.1-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"
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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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### Slot filling ###
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messages = [{"role": "user", "content": "우리가 뭘 사야되는데 원가가 57600원인데 직원할인 받으면 얼마야?"}]
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call(messages, model)
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# Output:
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# ChatCompletionMessage(content='직원 할인을 적용하기 위해서는 할인율을 알 수 있어야 합니다. 할인율을 알려주실 수 있나요?', role='assistant', function_call=None, tool_calls=[], reasoning_content=None)
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### Function calling ###
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messages = [
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{"role": "user", "content": "우리가 뭘 사야되는데 원가가 57600원인데 직원할인 받으면 얼마야?"},
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{"role": "assistant", "content": "직원 할인을 적용하기 위해서는 할인율을 알 수 있어야 합니다. 할인율을 알려주실 수 있나요?"},
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{"role": "user", "content": "15% 할인 받을 수 있어."},
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]
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call(messages, model)
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# Output:
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# ChatCompletionMessage(content=None, role='assistant', function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='chatcmpl-tool-3ebf11847364450daf363039db80cc50', function=Function(arguments='{"original_price": 57600, "discount_percentage": 15}', name='calculate_discount'), type='function')], reasoning_content=None)
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### Completion ###
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messages = [
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{"role": "user", "content": "우리가 뭘 사야되는데 원가가 57600원인데 직원할인 받으면 얼마야?"},
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{"role": "assistant", "content": ""},
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{"role": "user", "content": "15% 할인 받을 수 있어."},
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{"role": "tool", "tool_call_id": "random_id", "name": "calculate_discount", "content": "{\"original_price\": 57600, \"discount_percentage\": 15, \"discounted_price\": 48960.0}"}
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]
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call(messages, model)
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# Output:
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# ChatCompletionMessage(content='57,600원의 상품에 15% 할인을 적용하면, 할인된 가격은 48,960원입니다.', role='assistant', function_call=None, tool_calls=[], reasoning_content=None)
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```
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## License
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The `A.X 3.1 Light` model is licensed under `Apache License 2.0`.
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## Citation
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```
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@article{SKTAdotX3.1Light,
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title={A.X 3.1 Light},
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author={SKT AI Model Lab},
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year={2025},
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url={https://huggingface.co/skt/A.X-3.1-Light}
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}
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```
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## Contact
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- Business & Partnership Contact: [a.x@sk.com](a.x@sk.com) |