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初始化项目，由ModelHub XC社区提供模型 Model: skt/A.X-3.1-Light Source: Original Platform
2026-07-04 10:18:18 +08:00
---
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.
![image](./assets/A.X_3.1_Light_pareto.png)
## 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)
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