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+assets/A.X_logo_ko.jpg filter=lfs diff=lfs merge=lfs -text
+A.X_logo_ko.png filter=lfs diff=lfs merge=lfs -text
+assets/A.X_logo_ko.png filter=lfs diff=lfs merge=lfs -text
+assets/A.X_logo_ko_4x3.png filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,233 @@
+Copyright (c) 2025 SK Telecom Co., Ltd. All rights reserved.
+
+Built with Qwen 2.5 — original model by Alibaba Cloud, licensed under the Apache License 2.0.
+
+Unless otherwise stated, all files in this repository (including modified model weights 
+and tokenizer files) are distributed under the terms of the Apache License, Version 2.0 
+(the "License"). You may obtain a copy of the License at:
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under 
+the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF 
+ANY KIND, either express or implied. See the License for the specific language governing 
+permissions and limitations under the License.
+
+================================================================================
+NOTICE (Apache-2.0 §4 d)
+================================================================================
+
+This product is built with Qwen 2.5 developed by Alibaba Cloud 
+(https://github.com/QwenLM) and distributed under the Apache License 2.0.
+
+See the upstream Qwen 2.5 repository for additional attribution details.
+
+================================================================================
+TRADEMARK
+================================================================================
+
+"SK Telecom" and associated logos are trademarks of SK Telecom Co., Ltd. 
+This License does not grant permission to use these trademarks without prior 
+written consent.
+
+================================================================================
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README.md

@@ -1,3 +1,512 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+license_link: https://huggingface.co/skt/A.X-4.0-Light/blob/main/LICENSE
+language:
+- en
+- ko
+pipeline_tag: text-generation
+library_name: transformers
+model_id: skt/A.X-4.0-Light
+developers: SKT AI Model Lab
+model-index:
+- name: A.X-4.0-Light
+  results:
+  - task:
+      type: generate_until
+      name: mmlu
+    dataset:
+      name: mmlu (chat CoT)
+      type: hails/mmlu_no_train
+    metrics:
+    - type: exact_match
+      value: 75.43
+      name: exact_match
+  - task:
+      type: generate_until
+      name: kmmlu
+    dataset:
+      name: kmmlu (chat CoT)
+      type: HAERAE-HUB/KMMLU
+    metrics:
+    - type: exact_match
+      value: 64.15
+      name: exact_match
+---
+
+# A.X 4.0 Light
+
+<p align="center">
+    <picture>
+        <img src="./assets/A.X_logo_ko_4x3.png" width="45%" style="margin: 40px auto;">
+    </picture>
+</p>
+<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">🖥️ Github</a> </p>
+
+## A.X 4.0 Family Highlights
+
+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.
+
+- **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).
+- **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).
+- **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.
+- **Deployment Flexibility**: Offered in both a 72B-parameter standard model (A.X 4.0) and a 7B lightweight version (A.X 4.0 Light).
+- **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)
+
+## Performance
+
+### Model Performance
+
+<table><thead>
+  <tr>
+    <th colspan="2">Benchmarks</th>
+    <th>A.X 4.0</th>
+    <th>Qwen3-235B-A22B<br/>(w/o reasoning)</th>
+    <th>Qwen2.5-72B</th>
+    <th>GPT-4o</th>
+  </tr></thead>
+<tbody>
+  <tr>
+    <td rowspan="4">Knowledge</td>
+    <td>KMMLU</td>
+    <td>78.32</td>
+    <td>73.64</td>
+    <td>66.44</td>
+    <td>72.51</td>
+  </tr>
+  <tr>
+    <td>CLIcK</td>
+    <td>83.51</td>
+    <td>74.55</td>
+    <td>72.59</td>
+    <td>80.22</td>
+  </tr>
+  <tr>
+    <td>KoBALT</td>
+    <td>47.30</td>
+    <td>41.57</td>
+    <td>37.00</td>
+    <td>44.00</td>
+  </tr>
+  <tr>
+    <td>MMLU</td>
+    <td>86.62</td>
+    <td>87.37</td>
+    <td>85.70</td>
+    <td>88.70</td>
+  </tr>
+  <tr>
+    <td rowspan="3">General</td>
+    <td>Ko-MT-Bench</td>
+    <td>86.69</td>
+    <td>88.00</td>
+    <td>82.69</td>
+    <td>88.44</td>
+  </tr>
+  <tr>
+    <td>MT-Bench</td>
+    <td>83.25</td>
+    <td>86.56</td>
+    <td>93.50</td>
+    <td>88.19</td>
+  </tr>
+  <tr>
+    <td>LiveBench<sup>2024.11</sup></td>
+    <td>52.30</td>
+    <td>64.50</td>
+    <td>54.20</td>
+    <td>52.19</td>
+  </tr>
+  <tr>
+    <td rowspan="2">Instruction Following</td>
+    <td>Ko-IFEval</td>
+    <td>77.96</td>
+    <td>77.53</td>
+    <td>77.07</td>
+    <td>75.38</td>
+  </tr>
+  <tr>
+    <td>IFEval</td>
+    <td>86.05</td>
+    <td>85.77</td>
+    <td>86.54</td>
+    <td>83.86</td>
+  </tr>
+  <tr>
+    <td rowspan="2">Math</td>
+    <td>HRM8K</td>
+    <td>48.55</td>
+    <td>54.52</td>
+    <td>46.37</td>
+    <td>43.27</td>
+  </tr>
+  <tr>
+    <td>MATH</td>
+    <td>74.28</td>
+    <td>72.72</td>
+    <td>77.00</td>
+    <td>72.38</td>
+  </tr>
+  <tr>
+    <td rowspan="3">Code</td>
+    <td>HumanEval+</td>
+    <td>79.27</td>
+    <td>79.27</td>
+    <td>81.71</td>
+    <td>86.00</td>
+  </tr>
+  <tr>
+    <td>MBPP+</td>
+    <td>73.28</td>
+    <td>70.11</td>
+    <td>75.66</td>
+    <td>75.10</td>
+  </tr>
+  <tr>
+    <td>LiveCodeBench<sup>2024.10~2025.04</sup></td>
+    <td>26.07</td>
+    <td>33.09</td>
+    <td>27.58</td>
+    <td>29.30</td>
+  </tr>
+  <tr>
+    <td>Long Context</td>
+    <td>LongBench<sup>&lt;128K</sup></td>
+    <td>56.70</td>
+    <td>49.40</td>
+    <td>45.60</td>
+    <td>47.50</td>
+  </tr>
+  <tr>
+    <td>Tool-use</td>
+    <td>FunctionChatBench</td>
+    <td>85.96</td>
+    <td>82.43</td>
+    <td>88.30</td>
+    <td>95.70</td>
+  </tr>
+</tbody></table>
+
+### Lightweight Model Performance
+
+<table><thead>
+  <tr>
+    <th colspan="2">Benchmarks</th>
+    <th>A.X 4.0 Light</th>
+    <th>Qwen3-8B<br/>(w/o reasoning)</th>
+    <th>Qwen2.5-7B</th>
+    <th>EXAONE-3.5-7.8B</th>
+    <th>Kanana-1.5-8B</th>
+  </tr></thead>
+<tbody>
+  <tr>
+    <td rowspan="4">Knowledge</td>
+    <td>KMMLU</td>
+    <td>64.15</td>
+    <td>63.53</td>
+    <td>49.56</td>
+    <td>53.76</td>
+    <td>48.28</td>
+  </tr>
+  <tr>
+    <td>CLIcK</td>
+    <td>68.05</td>
+    <td>62.71</td>
+    <td>60.56</td>
+    <td>64.30</td>
+    <td>61.30</td>
+  </tr>
+  <tr>
+    <td>KoBALT</td>
+    <td>30.29</td>
+    <td>26.57</td>
+    <td>21.57</td>
+    <td>21.71</td>
+    <td>23.14</td>
+  </tr>
+  <tr>
+    <td>MMLU</td>
+    <td>75.43</td>
+    <td>82.89</td>
+    <td>75.40</td>
+    <td>72.20</td>
+    <td>68.82</td>
+  </tr>
+  <tr>
+    <td rowspan="3">General</td>
+    <td>Ko-MT-Bench</td>
+    <td>79.50</td>
+    <td>64.06</td>
+    <td>61.31</td>
+    <td>81.06</td>
+    <td>76.30</td>
+  </tr>
+  <tr>
+    <td>MT-Bench</td>
+    <td>81.56</td>
+    <td>65.69</td>
+    <td>79.37</td>
+    <td>83.50</td>
+    <td>77.60</td>
+  </tr>
+  <tr>
+    <td>LiveBench</td>
+    <td>37.10</td>
+    <td>50.20</td>
+    <td>37.00</td>
+    <td>40.20</td>
+    <td>29.40</td>
+  </tr>
+  <tr>
+    <td rowspan="2">Instruction Following</td>
+    <td>Ko-IFEval</td>
+    <td>72.99</td>
+    <td>73.39</td>
+    <td>60.73</td>
+    <td>65.01</td>
+    <td>69.96</td>
+  </tr>
+  <tr>
+    <td>IFEval</td>
+    <td>84.68</td>
+    <td>85.38</td>
+    <td>76.73</td>
+    <td>82.61</td>
+    <td>80.11</td>
+  </tr>
+  <tr>
+    <td rowspan="2">Math</td>
+    <td>HRM8K</td>
+    <td>40.12</td>
+    <td>52.50</td>
+    <td>35.13</td>
+    <td>31.88</td>
+    <td>30.87</td>
+  </tr>
+  <tr>
+    <td>MATH</td>
+    <td>68.88</td>
+    <td>71.48</td>
+    <td>65.58</td>
+    <td>63.20</td>
+    <td>59.28</td>
+  </tr>
+  <tr>
+    <td rowspan="3">Code</td>
+    <td>HumanEval+</td>
+    <td>75.61</td>
+    <td>77.44</td>
+    <td>74.39</td>
+    <td>76.83</td>
+    <td>76.83</td>
+  </tr>
+  <tr>
+    <td>MBPP+</td>
+    <td>67.20</td>
+    <td>62.17</td>
+    <td>68.50</td>
+    <td>64.29</td>
+    <td>67.99</td>
+  </tr>
+  <tr>
+    <td>LiveCodeBench</td>
+    <td>18.03</td>
+    <td>23.93</td>
+    <td>16.62</td>
+    <td>17.98</td>
+    <td>16.52</td>
+  </tr>
+</tbody></table>
+
+## 🚀 Quickstart
+
+### with HuggingFace Transformers
+
+- `transformers>=4.46.0` or the latest version is required to use `skt/A.X-4.0-Light`
+```bash
+pip install transformers>=4.46.0
+```
+
+#### Example Usage
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "skt/A.X-4.0-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 우주로 나가 지구를 공전했습니다.
+```
+
+### with vLLM
+
+- `vllm>=v0.6.4.post1` or the latest version is required to use tool-use function
+```bash
+pip install vllm>=v0.6.4.post1
+# if you don't want to activate tool-use function, just commenting out below vLLM option
+VLLM_OPTION="--enable-auto-tool-choice --tool-call-parser hermes"
+vllm serve skt/A.X-4.0-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-4.0-Light"
+messages = [{"role": "user", "content": "에어컨 여름철 적정 온도는? 한줄로 답변해줘"}]
+call(messages, model)
+# Output:
+# ChatCompletionMessage(content='여름철 적정 에어컨 온도는 일반적으로 24-26도입니다.', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[], reasoning_content=None)
+
+messages = [{"role": "user", "content": "What is the appropriate temperature for air conditioning in summer? Response in a single sentence."}]
+call(messages, model)
+# Output:
+# 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)
+```
+
+#### 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://ip:port/v1",
+    api_key="api_key"
+)
+model = "skt/A.X-4.0-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(예: 20% 할인의 경우 20을 입력)"
+                }
+            },
+            "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 원래 57600원인데 직원할인 받을 수 있거든? 할인가좀 계산해줘"}]
+call(messages, model)
+# Output:
+# 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])

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special_tokens_map.json

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+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|pad|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "<|sep|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|unk|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,389 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<|pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<|unk|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<|sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<|mask|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "<|cls|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "6": {
+      "content": "<|image|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "7": {
+      "content": "<|audio|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "8": {
+      "content": "<|user|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "9": {
+      "content": "<|system|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "10": {
+      "content": "<|assistant|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "11": {
+      "content": "<|extra_id_0|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "12": {
+      "content": "<|extra_id_1|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "13": {
+      "content": "<|extra_id_2|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "14": {
+      "content": "<|extra_id_3|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "15": {
+      "content": "<|extra_id_4|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "16": {
+      "content": "<|extra_id_5|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "17": {
+      "content": "<|extra_id_6|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "18": {
+      "content": "<|extra_id_7|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "19": {
+      "content": "<|extra_id_8|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "20": {
+      "content": "<|extra_id_9|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "21": {
+      "content": "<|extra_id_10|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "22": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "23": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "24": {
+      "content": "<|extra_id_13|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "25": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "26": {
+      "content": "<|im_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "27": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "28": {
+      "content": "<|resident_reg|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "29": {
+      "content": "<|foreigner_reg|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30": {
+      "content": "<|business_reg|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "31": {
+      "content": "<|credit_card|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32": {
+      "content": "<|passport|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "33": {
+      "content": "<|driver_license|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "34": {
+      "content": "<|telephone|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "35": {
+      "content": "<|health_insurance|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "36": {
+      "content": "<|bank_account|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "37": {
+      "content": "</tool_output>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "38": {
+      "content": "<tool_output>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "39": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "40": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|endoftext|>",
+    "<|pad|>",
+    "<|unk|>",
+    "<|sep|>",
+    "<|mask|>",
+    "<|cls|>",
+    "<|image|>",
+    "<|audio|>",
+    "<|user|>",
+    "<|system|>",
+    "<|assistant|>",
+    "<|extra_id_0|>",
+    "<|extra_id_1|>",
+    "<|extra_id_2|>",
+    "<|extra_id_3|>",
+    "<|extra_id_4|>",
+    "<|extra_id_5|>",
+    "<|extra_id_6|>",
+    "<|extra_id_7|>",
+    "<|extra_id_8|>",
+    "<|extra_id_9|>",
+    "<|extra_id_10|>",
+    "<|extra_id_13|>",
+    "<|im_start|>",
+    "<|im_sep|>",
+    "<|im_end|>",
+    "<|resident_reg|>",
+    "<|foreigner_reg|>",
+    "<|business_reg|>",
+    "<|credit_card|>",
+    "<|passport|>",
+    "<|driver_license|>",
+    "<|telephone|>",
+    "<|health_insurance|>",
+    "<|bank_account|>"
+  ],
+  "bos_token": "<|endoftext|>",
+  "chat_template": "{%- if tools is iterable and tools | length > 0 %}\n                    {{- '<|im_start|><|system|>'}}\n                    {{- '당신은 도구 호출 기능을 갖춘 유용한 도우미입니다. 사용자의 요청을 처리하기 위해서 필요한 도구가 주어진 목록에 있는 경우 도구 호출로 응답하세요.\n필요한 도구가 목록에 없는 경우에는 도구 호출 없이 사용자가 요구한 정보를 제공하세요.\n필요한 도구가 목록에 있지만 해당 도구를 호출하는데 필요한 argument 정보가 부족한 경우 해당 정보를 사용자에게 요청하세요.\n사용자의 요청을 처리하기 위해 여러번 도구를 호출할 수 있어야 합니다.\n도구 호출 이후 도구 실행 결과를 입력으로 받으면 해당 결과를 활용하여 답변을 생성하세요.\n\n다음은 접근할 수 있는 도구들의 목록 입니다:\n<tools>\n'}}\n                    {%- for t in tools %}\n                        {{- t | tojson }}\n                        {{- '\n' }}\n                    {%- endfor %}\n                    {{- '</tools>' }}\n                    {{- '\n\n도구를 호출하려면 아래의 JSON으로 응답하세요.\n도구 호출 형식: <tool_call>{\"name\": 도구 이름, \"arguments\": dictionary 형태의 도구 인자값}</tool_call>' }}\n                    {{- '<|im_end|>' }}\n                {%- endif %}\n        \n                {%- for message in messages %}\n                    {%- if message.role == 'system' %}\n                        {{- '<|im_start|><|system|>' + message.content + '<|im_end|>'}}\n                    {%- elif message.role == 'user' %}\n                        {{- '<|im_start|><|user|>' + message.content + '<|im_end|>'}}\n                    {%- elif message.role == 'assistant' %}\n                        {{- '<|im_start|><|assistant|>'}}\n                        {%- set content = '' %}\n                        {%- if message.content is defined %}\n                            {%- set content = message.content %}\n                        {%- endif %}\n                        \n                {%- if add_generation_prompt and not (message.reasoning_content is defined and message.reasoning_content is not none) %}\n                    {%- if '</think>' in message.content %}\n                        {%- set content = message.content.split('</think>'.strip())[-1].lstrip('\\n') %}\n                    {%- endif %}\n                {%- endif %}\n                \n                        {{- content}}\n                        {%- if message.tool_calls is defined %}\n                            {%- for tool_call in message.tool_calls %}\n                                {%- if tool_call.function is defined %}\n                                    {%- set tool_call = tool_call.function %}\n                                {%- endif %}\n                                {{- '<tool_call>' }}\n                                {{- '{' }}\n                                {{- '\"name\": \"' }}\n                                {{- tool_call.name }}\n                                {{- '\"' }}\n                                {%- if tool_call.arguments is defined %}\n                                    {{- ', ' }}\n                                    {{- '\"arguments\": ' }}\n                                    {{- tool_call.arguments|tojson }}\n                                {%- endif %}\n                                {{- '}' }}\n                                {{- '</tool_call>' }}\n                            {%- endfor %}\n                        {%- endif %}\n                        {{- '<|im_end|>'}}\n        \n                    {%- elif message.role == 'tool' %}\n                        {{- '<|im_start|><|extra_id_13|><tool_output>' + message.content + '</tool_output><|im_end|>'}}\n                    {%- endif %}\n                {%- endfor %}\n                \n                    {%- if add_generation_prompt %}\n                        {{- '<|im_start|><|assistant|>' }}\n                    {%- endif %}",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<|cls|>",
+  "eod_token": "<|endoftext|>",
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "mask_token": "<|mask|>",
+  "max_length": 7680,
+  "model_max_length": 16384,
+  "pad_token": "<|pad|>",
+  "padding_side": "right",
+  "sep_token": "<|sep|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "truncation_side": "left",
+  "unk_token": "<|unk|>",
+  "vocab_size": 102400
+}