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Model: mykor/A.X-3.1-Light-gguf Source: Original Platform
2026-08-30 04:31:16 +08:00
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+Copyright (c) 2025 SK Telecom Co., Ltd. All rights reserved.
+
+Unless otherwise stated, all files in this repository (including modified model weights 
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+Unless required by applicable law or agreed to in writing, software distributed under 
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--- a/README.md
+++ b/README.md
@@ -0,0 +1,410 @@
+---
+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
+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: 61.7
+      name: exact_match
+base_model:
+- skt/A.X-3.1-Light
+tags:
+- A.X
+---
+
+# A.X 3.1 Light
+
+<div align="center">
+  <img src="https://i.imgur.com/xthLxve.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="4">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>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)
--- a/imatrix.dat
+++ b/imatrix.dat
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