| --- |
| license: apache-2.0 |
| license_link: https://huggingface.co/skt/A.X-3.1-Light/blob/main/LICENSE |
| language: |
| - en |
| - ko |
| pipeline_tag: text-generation |
| library_name: transformers |
| model_id: skt/A.X-3.1-Light |
| developers: SKT AI Model Lab |
| model-index: |
| - name: A.X-3.1-Light |
| results: |
| - task: |
| type: generate_until |
| name: mmlu |
| dataset: |
| name: mmlu (chat CoT) |
| type: hails/mmlu_no_train |
| metrics: |
| - type: exact_match |
| value: 66.95 |
| name: exact_match |
| - task: |
| type: generate_until |
| name: kmmlu |
| dataset: |
| name: kmmlu (chat CoT) |
| type: HAERAE-HUB/KMMLU |
| metrics: |
| - type: exact_match |
| value: 61.70 |
| name: exact_match |
| --- |
| |
| # A.X 3.1 Light |
|
|
| <div align="center"> |
| <img src="./assets/A.X_from_scratch_logo_ko_4x3.png" alt="A.X Logo" width="300"/> |
| </div> |
| <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> |
|
|
| ## A.X 3.1 Light Highlights |
|
|
| <!-- 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. --> |
| **A.X 3.1 Light** (pronounced "A dot X") is a light weight LLM optimized for Korean-language understanding and enterprise deployment. |
| 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. |
| 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. |
| 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**. |
|
|
|
|
| - **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. |
| - **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. |
| - **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. |
| - **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. |
| - **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. |
| - **Long-Context Handling**: A.X 3.1 Light supports up to **32,768 tokens**. |
|
|
|
|
| ## Core Technologies |
|
|
| 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. |
|
|
| ### Model Architecture Specs |
|
|
| <table><thead> |
| <tr> |
| <th>Model</th> |
| <th># Params</th> |
| <th># Layers</th> |
| <th># KV-Heads</th> |
| <th>Hidden Dim</th> |
| <th>FFN Dim</th> |
| </tr> |
| <tr> |
| <th>A.X 3.1 Light</th> |
| <th>7B</th> |
| <th>32</th> |
| <th>32</th> |
| <th>4096</th> |
| <th>10880</th> |
| </tr> |
| </thead> |
| </table> |
| |
| ### High-Quality Data Pipeline & Strategic Mixture |
|
|
| - We collected and curated a training dataset comprising 20 trillion tokens sourced from diverse domains. |
| - The entire dataset was processed through SKT’s proprietary data pipeline, incorporating synthetic data generation and comprehensive quality filtering. |
| - For training A.X 3.1 Light, a total of **1.65 trillion tokens** were utilized, comprising a Korean-focused multilingual corpus. |
|
|
| ### Pareto-Optimal Compute Efficiency |
|
|
| A.X 3.1 Light achieves 5 to 6 times lower computational cost compared to models with similar performance levels. |
| Rigorous data curation and two-stage training with STEM-focused data enabled competitive performance at reduced FLOPs. |
|
|
|
|
|  |
|
|
| ## Benchmark Results |
|
|
| <table><thead> |
| <tr> |
| <th colspan="2">Benchmarks</th> |
| <th>A.X 3.1 Light</th> |
| <th>Kanana-1.5-8B</th> |
| <th>EXAONE-3.5-7.8B</th> |
| <th>Qwen2.5-7B</th> |
| <th>Qwen3-8B<br>(w/o reasoning)</th> |
| </tr></thead> |
| <tbody> |
| <tr> |
| <td rowspan="6">Knowledge</td> |
| <td>KMMLU</td> |
| <td>61.70</td> |
| <td>48.28</td> |
| <td>53.76</td> |
| <td>49.56</td> |
| <td>63.53</td> |
| </tr> |
| <tr> |
| <td>KMMLU-pro</td> |
| <td>45.54</td> |
| <td>37.63</td> |
| <td>40.11</td> |
| <td>38.87</td> |
| <td>50.71</td> |
| </tr> |
| <tr> |
| <td>KMMLU-redux</td> |
| <td>52.34</td> |
| <td>35.33</td> |
| <td>42.21</td> |
| <td>38.58</td> |
| <td>55.74</td> |
| </tr> |
| <tr> |
| <td>CLIcK</td> |
| <td>71.22</td> |
| <td>61.30</td> |
| <td>64.11</td> |
| <td>58.30</td> |
| <td>63.31</td> |
| </tr> |
| <tr> |
| <td>KoBALT</td> |
| <td>27.43</td> |
| <td>23.14</td> |
| <td>21.71</td> |
| <td>21.57</td> |
| <td>26.57</td> |
| </tr> |
| <tr> |
| <td>MMLU</td> |
| <td>66.95</td> |
| <td>68.82</td> |
| <td>72.20</td> |
| <td>75.40</td> |
| <td>82.89</td> |
| </tr> |
| <tr> |
| <td rowspan="2">General</td> |
| <td>Ko-MT-Bench</td> |
| <td>78.56</td> |
| <td>76.30</td> |
| <td>81.06</td> |
| <td>61.31</td> |
| <td>64.06</td> |
| </tr> |
| <tr> |
| <td>MT-Bench</td> |
| <td>74.38</td> |
| <td>77.60</td> |
| <td>83.50</td> |
| <td>79.37</td> |
| <td>65.69</td> |
| </tr> |
| <tr> |
| <td rowspan="2">Instruction<br>Following</td> |
| <td>Ko-IFEval</td> |
| <td>70.04</td> |
| <td>69.96</td> |
| <td>65.01</td> |
| <td>60.73</td> |
| <td>73.39</td> |
| </tr> |
| <tr> |
| <td>IFEval</td> |
| <td>79.86</td> |
| <td>80.11</td> |
| <td>82.61</td> |
| <td>76.73</td> |
| <td>85.38</td> |
| </tr> |
| <tr> |
| <td rowspan="2">Math</td> |
| <td>HRM8K</td> |
| <td>41.70</td> |
| <td>30.87</td> |
| <td>31.88</td> |
| <td>35.13</td> |
| <td>52.50</td> |
| </tr> |
| <tr> |
| <td>MATH</td> |
| <td>70.14</td> |
| <td>59.28</td> |
| <td>63.20</td> |
| <td>65.58</td> |
| <td>71.48</td> |
| </tr> |
| <tr> |
| <td rowspan="2">Code<br></td> |
| <td>HumanEval+</td> |
| <td>73.78</td> |
| <td>76.83</td> |
| <td>76.83</td> |
| <td>74.39</td> |
| <td>77.44</td> |
| </tr> |
| <tr> |
| <td>MBPP+</td> |
| <td>61.64</td> |
| <td>67.99</td> |
| <td>64.29</td> |
| <td>68.50</td> |
| <td>62.17</td> |
| </tr> |
| </tbody></table> |
| |
| ## 🚀 Quickstart |
|
|
| ### with HuggingFace Transformers |
|
|
| - `transformers>=4.46.0` or the latest version is required to use `skt/A.X-3.1-Light` |
| ```bash |
| pip install transformers>=4.46.0 |
| ``` |
|
|
| #### Example Usage |
|
|
| ```python |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model_name = "skt/A.X-3.1-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 우주에 나가 지구를 궤도를 돌았습니다. |
| ``` |
|
|
| ### with vLLM |
|
|
| - `vllm>=v0.6.4.post1` or the latest version is required to use tool-use feature |
| ```bash |
| pip install vllm>=v0.6.4.post1 |
| # if you don't want to activate tool-use feature, just commenting out below vLLM option |
| VLLM_OPTION="--enable-auto-tool-choice --tool-call-parser hermes" |
| vllm serve skt/A.X-3.1-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-3.1-Light" |
| messages = [{"role": "user", "content": "에어컨 여름철 적정 온도는? 한줄로 답변해줘"}] |
| call(messages, model) |
| # Output: |
| # 에어컨 여름철 적정 온도는 24~26도입니다. |
| |
| messages = [{"role": "user", "content": "What is the appropriate temperature for air conditioning in summer? Respond in a single sentence."}] |
| call(messages, model) |
| # Output: |
| # The appropriate temperature for air conditioning in summer is generally set between 24 to 26°C for optimal comfort and energy efficiency. |
| ``` |
|
|
| #### 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-3.1-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" |
| } |
| }, |
| "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 원가가 57600원인데 직원할인 받으면 얼마야?"}] |
| call(messages, model) |
| # Output: |
| # 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) |
| |
| |
| ### Function calling ### |
| messages = [ |
| {"role": "user", "content": "우리가 뭘 사야되는데 원가가 57600원인데 직원할인 받으면 얼마야?"}, |
| {"role": "assistant", "content": "직원 할인을 적용하기 위해서는 할인율을 알 수 있어야 합니다. 할인율을 알려주실 수 있나요?"}, |
| {"role": "user", "content": "15% 할인 받을 수 있어."}, |
| ] |
| call(messages, model) |
| # Output: |
| # 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) |
| |
| |
| ### Completion ### |
| messages = [ |
| {"role": "user", "content": "우리가 뭘 사야되는데 원가가 57600원인데 직원할인 받으면 얼마야?"}, |
| {"role": "assistant", "content": ""}, |
| {"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='57,600원의 상품에 15% 할인을 적용하면, 할인된 가격은 48,960원입니다.', role='assistant', function_call=None, tool_calls=[], reasoning_content=None) |
| ``` |
|
|
| ## License |
|
|
| The `A.X 3.1 Light` model is licensed under `Apache License 2.0`. |
|
|
| ## Citation |
| ``` |
| @article{SKTAdotX3.1Light, |
| title={A.X 3.1 Light}, |
| author={SKT AI Model Lab}, |
| year={2025}, |
| url={https://huggingface.co/skt/A.X-3.1-Light} |
| } |
| ``` |
|
|
| ## Contact |
|
|
| - Business & Partnership Contact: [a.x@sk.com](a.x@sk.com) |
