Initial upload

LICENSE.txt

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+MIT License
+
+Copyright (c) 2025 KT Corporation
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+license: mit
+language:
+- en
+- ko
+tags:
+- KT
+- K-intelligence
+- Mi:dm
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+<p align="center">
+<br>
+    <span style="font-size: 60px; font-weight: bold;">Mi:dm 2.0-Mini</span>
+</br>
+</p>
+<p align="center">
+🤗 <a href="">Mi:dm 2.0 Models</a> |
+📜 Mi:dm 2.0 Technical Report* |
+📕 Mi:dm 2.0 Technical Blog*
+</p>
+
+<p align="center"><sub>*To be released soon</sub></p>
+
+<br>
+
+## News 📢
+
+- 🔜 _(Coming Soon!) GGUF format model files will be available soon for easier local deployment._  
+- ⚡️`2025/07/04`: Released Mi:dm 2.0 Model collection on Hugging Face🤗.
+<br>
+<br>
+# Table of Contents
+
+- ___Overview___
+    - [Mi:dm 2.0](#midm-20)
+    - [Quickstart](#quickstart)
+    - [Evaluation](#evaluation)
+- ___Usage___
+    - [Run on Friendly.AI](#run-on-friendliai)
+    - [Run on Your Local Machine](#run-on-your-local-machine)
+    - [Deployment](#deployment)
+    - [Tutorials](#tutorials)
+- ___More Information___
+    - [Limitation](#limitation)
+    - [License](#license)
+    - [Contact](#contact)
+
+<br>
+<br>
+
+# Overview
+
+### Mi:dm 2.0
+
+Mi:dm 2.0 is a __"Korean-centric AI"__ model developed with KT's proprietary technology. __"Korean-centric AI"__ refers to a model that thoroughly internalizes the unique values, cognitive frameworks, and commonsense reasoning intrinsic to Korean society. It is not simply about processing and responding in Korean; it is about the profound understanding that reflects and respects the socio-cultural fabric of Korean norms and values.
+
+The newly introduced Mi:dm 2.0 model comes in two versions:
+
+* **Mi:dm 2.0-Mini** is a 2.3B parameter Dense small model, designed for seamless use in environments such as on-device settings and low-end GPUs. It was created by pruning and distilling the Base model.
+
+* **Mi:dm 2.0-Base** has 11.5B parameters and was designed to balance model size and performance by expanding an 8B scale model using the DuS (Depth-up Scaling) method. It's a practical model that can be applied to various real-world services, considering both performance and versatility.
+
+
+> [!Note]
+> Neither the pre-training nor the post-training data includes KT users' data.
+
+
+<br>
+
+### Quickstart
+
+Here is the code snippet to run conversational inference with the model:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+
+model_name = "K-intelligence/Midm-2.0-Mini-Instruct"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+generation_config = GenerationConfig.from_pretrained(model_name)
+
+prompt = "KT에 대해 소개해줘"
+
+# message for inference
+messages = [
+    {"role": "system", 
+     "content": "Mi:dm(믿:음)은 KT에서 개발한 AI 기반 어시스턴트이다."},
+    {"role": "user", "content": prompt}
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt"
+)
+
+output = model.generate(
+    input_ids.to("cuda"),
+    generation_config=generation_config,
+    eos_token_id=tokenizer.eos_token_id,
+    max_new_tokens=128,
+    do_sample=False,
+)
+print(tokenizer.decode(output[0]))
+```
+
+> [!NOTE]
+> The `transformers` library should be version `4.45.0` or higher.
+
+<br>
+<br>
+
+# Evaluation
+
+#### English
+<table>
+  <thead>
+    <tr>
+      <th colspan="2"><b>Benchmark</b></th>
+      <th>Exaone-3.5-2.4B-inst</th>
+      <th>Qwen3-4B</th>
+      <th>Mi:dm 2.0-Mini-inst</th>
+      <th>Exaone-3.5-7.8B-inst</th>
+      <th>Qwen3-14B</th>
+      <th>Llama-3.1-8B-inst</th>
+      <th>Mi:dm 2.0-Base-inst</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td rowspan="1"><b>Instruction Following</b></td>
+      <td><b>IFEval</b></td>
+      <td align="center">81.1</td>
+      <td align="center">79.7</td>
+      <td align="center">73.6</td>
+      <td align="center">83.6</td>
+      <td align="center">83.9</td>
+      <td align="center">79.9</td>
+      <td align="center"><b>84.0</b></td>
+    </tr>
+    <tr>
+      <td rowspan="4"><b>Reasoning</b></td>
+      <td><b>BBH</b></td>
+      <td align="center">46.4</td>
+      <td align="center">79.0</td>
+      <td align="center">44.5</td>
+      <td align="center">50.1</td>
+      <td align="center">83.4</td>
+      <td align="center">60.3</td>
+      <td align="center"><b>77.7</b></td>
+    </tr>
+    <tr>
+      <td><b>GPQA</b></td>
+      <td align="center">28.1</td>
+      <td align="center">39.8</td>
+      <td align="center">26.6</td>
+      <td align="center">33.1</td>
+      <td align="center">49.8</td>
+      <td align="center">21.6</td>
+      <td align="center"><b>33.5</b></td>
+    </tr>
+    <tr>
+      <td><b>MuSR</b></td>
+      <td align="center">49.7</td>
+      <td align="center">58.5</td>
+      <td align="center">51.7</td>
+      <td align="center">51.2</td>
+      <td align="center">57.7</td>
+      <td align="center">50.3</td>
+      <td align="center"><b>51.9</b></td>
+    </tr>
+    <tr>
+      <td><b>Avg.</b></td>
+      <td align="center">41.4</td>
+      <td align="center">59.1</td>
+      <td align="center">40.9</td>
+      <td align="center">44.8</td>
+      <td align="center">63.6</td>
+      <td align="center">44.1</td>
+      <td align="center"><b>54.4</b></td>
+    </tr>
+    <tr>
+      <td rowspan="2"><b>Mathematics</b></td>
+      <td><b>GSM8K</b></td>
+      <td align="center">82.5</td>
+      <td align="center">90.4</td>
+      <td align="center">83.1</td>
+      <td align="center">81.1</td>
+      <td align="center">88.0</td>
+      <td align="center">81.2</td>
+      <td align="center"><b>91.6</b></td>
+    </tr>
+    <tr>
+      <td><b>MBPP+</b></td>
+      <td align="center">59.8</td>
+      <td align="center">62.4</td>
+      <td align="center">60.9</td>
+      <td align="center">79.4</td>
+      <td align="center">73.4</td>
+      <td align="center">81.8</td>
+      <td align="center"><b>77.5</b></td>
+    </tr>
+    <tr>
+      <td rowspan="3"><b>General Knowledge</b></td>
+      <td><b>MMLU-pro</b></td>
+      <td align="center">-</td>
+      <td align="center">-</td>
+      <td align="center">-</td>
+      <td align="center">40.7</td>
+      <td align="center">70.5</td>
+      <td align="center">47.6</td>
+      <td align="center"><b>53.3</b></td>
+    </tr>
+    <tr>
+      <td><b>MMLU</b></td>
+      <td align="center">59.5</td>
+      <td align="center">73.3</td>
+      <td align="center">56.5</td>
+      <td align="center">69.0</td>
+      <td align="center">82.7</td>
+      <td align="center">70.7</td>
+      <td align="center"><b>73.7</b></td>
+    </tr>
+    <tr>
+      <td><b>Avg.</b></td>
+      <td align="center">59.5</td>
+      <td align="center">73.3</td>
+      <td align="center">56.5</td>
+      <td align="center">54.8</td>
+      <td align="center"><b>76.6</b></td>
+      <td align="center">59.2</td>
+      <td align="center">63.5</td>
+    </tr>
+  </tbody>
+</table>
+
+#### Korean
+<table>
+  <thead>
+    <tr>
+      <th colspan="2"><b>Benchmark</b></th>
+      <th>Exaone-3.5-2.4B-inst</th>
+      <th>Qwen3-4B</th>
+      <th>Mi:dm 2.0-Mini-inst</th>
+      <th>Exaone-3.5-7.8B-inst</th>
+      <th>Qwen3-14B</th>
+      <th>Llama-3.1-8B-inst</th>
+      <th>Mi:dm 2.0-Base-inst</th>
+    </tr>
+  </thead>
+  <tbody>
+    <!-- Comprehension -->
+    <tr>
+      <td rowspan="5"><b>Comprehension</b></td>
+      <td><b>K-Prag*</b></td>
+      <td align="center">68.7</td>
+      <td align="center">73.9</td>
+      <td align="center">69.5</td>
+      <td align="center">73.5</td>
+      <td align="center"><b>86.7</b></td>
+      <td align="center">59.9</td>
+      <td align="center">86.5</td>
+    </tr>
+    <tr>
+      <td><b>K-Refer-Hard*</b></td>
+      <td align="center">58.5</td>
+      <td align="center">56.7</td>
+      <td align="center">55.4</td>
+      <td align="center">61.9</td>
+      <td align="center"><b>74.0</b></td>
+      <td align="center">48.6</td>
+      <td align="center">70.8</td>
+    </tr>
+    <tr>
+      <td><b>Ko-Best</b></td>
+      <td align="center">87.2</td>
+      <td align="center">91.5</td>
+      <td align="center">80.5</td>
+      <td align="center">92.0</td>
+      <td align="center">93.9</td>
+      <td align="center">77.4</td>
+      <td align="center"><b>95.2</b></td>
+    </tr>
+    <tr>
+      <td><b>Ko-Sovereign*</b></td>
+      <td align="center">38.0</td>
+      <td align="center">43.5</td>
+      <td align="center">42.5</td>
+      <td align="center">44.0</td>
+      <td align="center">52.0</td>
+      <td align="center">31.5</td>
+      <td align="center"><b>53.0</b></td>
+    </tr>
+    <tr>
+      <td><b>Avg.</b></td>
+      <td align="center">62.5</td>
+      <td align="center">66.6</td>
+      <td align="center">61.9</td>
+      <td align="center">67.2</td>
+      <td align="center"><b>76.8</b></td>
+      <td align="center">51.5</td>
+      <td align="center">76.1</td>
+    </tr>
+    <tr>
+      <td rowspan="5"><b>Reasoning</b></td>
+      <td><b>Ko-Winogrande</b></td>
+      <td align="center">60.3</td>
+      <td align="center"><b>67.5</b></td>
+      <td align="center">61.7</td>
+      <td align="center">64.6</td>
+      <td align="center">77.2</td>
+      <td align="center">40.1</td>
+      <td align="center">75.1</td>
+    </tr>
+    <tr>
+      <td><b>Ko-Best</b></td>
+      <td align="center">64.1</td>
+      <td align="center"><b>69.2</b></td>
+      <td align="center">64.5</td>
+      <td align="center">60.3</td>
+      <td align="center">75.4</td>
+      <td align="center">26.0</td>
+      <td align="center">73.0</td>
+    </tr>
+    <tr>
+      <td><b>LogicKor*</b></td>
+      <td align="center"><b>7.4</b></td>
+      <td align="center">5.6</td>
+      <td align="center">7.7</td>
+      <td align="center">8.6</td>
+      <td align="center">6.4</td>
+      <td align="center">2.4</td>
+      <td align="center">8.6</td>
+    </tr>
+    <tr>
+      <td><b>HRM8K*</b></td>
+      <td align="center">38.5</td>
+      <td align="center"><b>56.7</b></td>
+      <td align="center">39.9</td>
+      <td align="center">49.7</td>
+      <td align="center">64.5</td>
+      <td align="center">30.9</td>
+      <td align="center">52.9</td>
+    </tr>
+    <tr>
+      <td><b>Avg.</b></td>
+      <td align="center">36.7</td>
+      <td align="center"><b>43.8</b></td>
+      <td align="center">37.4</td>
+      <td align="center">39.5</td>
+      <td align="center">48.8</td>
+      <td align="center">19.8</td>
+      <td align="center">44.8</td>
+    </tr>
+    <!-- Society & Culture -->
+    <tr>
+      <td rowspan="5"><b>Society & Culture</b></td>
+      <td><b>K-Refer*</b></td>
+      <td align="center">64.0</td>
+      <td align="center">53.6</td>
+      <td align="center">66.4</td>
+      <td align="center">71.6</td>
+      <td align="center">72.4</td>
+      <td align="center">43.2</td>
+      <td align="center"><b>89.6</b></td>
+    </tr>
+    <tr>
+      <td><b>K-Refer-Hard*</b></td>
+      <td align="center">67.1</td>
+      <td align="center">42.9</td>
+      <td align="center">61.4</td>
+      <td align="center">69.3</td>
+      <td align="center">65.7</td>
+      <td align="center">36.4</td>
+      <td align="center"><b>86.4</b></td>
+    </tr>
+    <tr>
+      <td><b>Ko-Sovereign*</b></td>
+      <td align="center">44.4</td>
+      <td align="center">35.8</td>
+      <td align="center">36.7</td>
+      <td align="center">46.9</td>
+      <td align="center"><b>49.8</b></td>
+      <td align="center">33.8</td>
+      <td align="center">56.3</td>
+    </tr>
+    <tr>
+      <td><b>HAERAE*</b></td>
+      <td align="center">61.3</td>
+      <td align="center">50.6</td>
+      <td align="center">70.8</td>
+      <td align="center">72.9</td>
+      <td align="center">68.4</td>
+      <td align="center">49.5</td>
+      <td align="center"><b>81.5</b></td>
+    </tr>
+    <tr>
+      <td><b>Avg.</b></td>
+      <td align="center">59.2</td>
+      <td align="center">45.7</td>
+      <td align="center">58.8</td>
+      <td align="center">65.2</td>
+      <td align="center">64.1</td>
+      <td align="center">40.7</td>
+      <td align="center"><b>78.4</b></td>
+    </tr>
+    <!-- Reasoning (Domain) -->
+    <tr>
+      <td rowspan="3"><b>Reasoning (Domain)</b></td>
+      <td><b>KMMLU</b></td>
+      <td align="center">43.5</td>
+      <td align="center">50.6</td>
+      <td align="center">45.1</td>
+      <td align="center">52.6</td>
+      <td align="center">55.4</td>
+      <td align="center">33.0</td>
+      <td align="center"><b>57.3</b></td>
+    </tr>
+    <tr>
+      <td><b>Ko-Sovereign*</b></td>
+      <td align="center">42.4</td>
+      <td align="center">42.5</td>
+      <td align="center">42.4</td>
+      <td align="center">45.6</td>
+      <td align="center">54.7</td>
+      <td align="center">36.7</td>
+      <td align="center"><b>58.0</b></td>
+    </tr>
+    <tr>
+      <td><b>Avg.</b></td>
+      <td align="center">43.0</td>
+      <td align="center">46.5</td>
+      <td align="center">43.8</td>
+      <td align="center">49.1</td>
+      <td align="center">55.1</td>
+      <td align="center">34.8</td>
+      <td align="center"><b>57.7</b></td>
+    </tr>
+    <!-- Instruction Following -->
+    <tr>
+      <td rowspan="3"><b>Instruction Following</b></td>
+      <td><b>Ko-IFEval*</b></td>
+      <td align="center">65.4</td>
+      <td align="center">75.9</td>
+      <td align="center">73.3</td>
+      <td align="center">69.1</td>
+      <td align="center"><b>83.6</b></td>
+      <td align="center">60.1</td>
+      <td align="center">82.0</td>
+    </tr>
+    <tr>
+      <td><b>Ko-MTBench</b></td>
+      <td align="center">74.0</td>
+      <td align="center">63.0</td>
+      <td align="center">74.0</td>
+      <td align="center">79.6</td>
+      <td align="center">71.0</td>
+      <td align="center">57.0</td>
+      <td align="center"><b>89.7</b></td>
+    </tr>
+    <tr>
+      <td><b>Avg.</b></td>
+      <td align="center">68.9</td>
+      <td align="center">69.4</td>
+      <td align="center">73.6</td>
+      <td align="center">74.4</td>
+      <td align="center">77.3</td>
+      <td align="center">58.5</td>
+      <td align="center"><b>85.9</b></td>
+    </tr>
+  </tbody>
+</table>
+
+`*` indicates KT proprietary evaluation resources.
+
+<br>
+
+# Usage
+
+### Run on Friendli.AI
+You can try our model immediately via `Friendli.AI`. Simply click `Deploy` and then `Friendli Endpoints`. 
+
+> [!Note]
+> Please note that a login to `Friendli.AI` is required after your fifth chat interaction.
+
+<p>
+  <img src="./assets/image_1.png" alt="Left Image" width="36%" style="display:inline-block; margin-right:2%">
+  <img src="./assets/image_2.png" alt="Right Image" width="36%" style="display:inline-block">
+</p>
+
+### Run on Your Local Machine
+We provide a detailed description about running Mi:dm 2.0 on your local machine using llama.cpp, LM Studio, and Ollama. Please check our [github]() for more information
+
+
+### Deployment
+
+To serve Mi:dm 2.0 using [vLLM](https://github.com/vllm-project/vllm)(`>=0.8.0`) with an OpenAI-compatible API:
+```bash
+vllm serve K-intelligence/Midm-2.0-Mini-Instruct
+```
+
+
+### Tutorials
+To help our end-users easily use Mi:dm 2.0, we have provided comprehensive tutorials on [github](). 
+<br>
+
+<br>
+<br>
+
+# More Information
+
+### Limitation
+* The training data for both Mi:dm 2.0 models consists primarily of English and Korean. Understanding and generation in other languages are not guaranteed.
+  
+* The model is not guaranteed to provide reliable advice in fields that require professional expertise, such as law, medicine, or finance.
+
+* Researchers have made efforts to exclude unethical content from the training data — such as profanity, slurs, bias, and discriminatory language. However, despite these efforts, the model may still produce inappropriate expressions or factual inaccuracies.
+
+
+### License
+
+Mi:dm 2.0 is licensed under the [MIT License](./LICENSE).
+ 
+<!-- ### Citation
+ 
+```
+@misc{,
+      title={}, 
+      author={},
+      year={2025},
+      eprint={},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={}, 
+}
+``` -->
+### Contact 
+- Mi:dm 2.0 Technical Inquiries: [email protected]
+
+<br>

config.json

@@ -0,0 +1,31 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 1792,
+  "initializer_range": 0.02,
+  "intermediate_size": 4608,
+  "max_position_embeddings": 32768,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 8000000,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.48.2",
+  "use_cache": true,
+  "vocab_size": 131392
+}

generation_config.json

@@ -0,0 +1,10 @@
+{
+  "bos_token_id": 0,
+  "do_sample": true,
+  "eos_token_id": 2,
+  "repetition_penalty": 1.0,
+  "temperature": 0.8,
+  "top_k": 20,
+  "top_p": 0.75,
+  "transformers_version": "4.48.2"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:03755deae6cc183c9957bcf7b753924b0d1284e1b44aea95aaa822594765e70b
+size 4611084960

special_tokens_map.json

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+{
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+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n\n{%- if not date_string is defined %}\n    {%- if strftime_now is defined %}\n        {%- set date_string = strftime_now('%d %b %Y') %}\n    {%- else %}\n        {%- set date_string = '04 Jul 2025' %}\n    {%- endif %}\n{%- endif %}\n\n{%- if messages[0].role == \"system\" %}\n    {%- set system_message = messages[0].content | trim %}\n    {%- set messages = messages[1:] %}\n{%- endif %}\n\n{{- '<|start_header_id|>system<|end_header_id|>\\n\\n' }}\n{%- if tools is not none %}\n    {{- 'Environment: ipython\\n' }}\n{%- endif %}\n\n{{- 'Cutting Knowledge Date: December 2024\\n' }}\n{{- 'Today Date: ' + date_string + '\\n\\n' }}\n{{- 'Mi:dm(믿:음)은 KT에서 개발한 AI 기반 어시스턴트이다. 너는 Mi:dm으로서 사용자에게 유용하고 안전한 응답을 제공해야 한다.\\n\\n' }}\n{{- 'Mi:dm은 December 2024까지의 지식으로 학습되었으며 그 외의 지식을 묻는 경우에는 한계를 인정해야 한다.\\n' }}\n{{- '오늘 날짜는 ' + date_string + '임을 참고하여 응답을 제공한다.\\n\\n' }}\n{{- '어시스턴트는 기본적으로 \"한국어\"를 사용한다. 사용자의 요청에 따라 생각하고 응답하는 언어는 달라질 수 있으며, 다른 요구사항이 없다면 입력 언어를 따라 응답하라.\\n' }}\n{{- '코드 작성 시에는 요구되는 언어의 소스코드로 작성해야 하며, STEM(과학, 기술, 공학, 수학) 분야의 전문 용어는 원문을 그대로 유지하여 출력한다.\\n\\n' }}\n{{- 'Mi:dm은 사용자 친화적으로 답변을 제공해야 한다. 사용자의 요청이 없다면 기본적으로 경어체를 사용해야 한다.\\n' }}\n{{- '사용자의 요청에 따라 유용하고 관련성 있는 답변을 제공해야 한다. 이는 요청의 내용을 반영하여 이루어져야 한다.\\n' }}\n{{- '특히, 사용자가 특정 답변의 형태를 요구한 경우 해당 형태를 반드시 준수하여 응답을 제공한다.(예. JSON, SQL, 코드 등) 그렇지 않다면 응답을 제공하기 가장 적절한 형태로 제공한다.\\n' }}\n{{- '다양한 시각이 존재하는 주제에 대해서는 중립적인 입장을 유지해야 하지만, 한국 특화 인공지능 어시스턴트로서 정치적, 역사적, 외교적으로 한국인의 입장에서 응답을 생성해야 한다.\\n' }}\n{{- '모호하거나 복잡한 요청으로 인해 답변 작성이 어려운 경우, 다시 한 번 생각해보고 추가정보를 요청해야 한다.\\n\\n' }}\n{{- 'Mi:dm은 응답을 제공할 때 어시스턴트의 안전성 측면에서 다음 지침을 *반드시* 준수해야 한다.\\n' }}\n{{- '- 비속어와 욕설을 사용하지 않아야 한다.\\n' }}\n{{- '- 신뢰할 수 있는 응답을 생성하고, 전문영역에 대한 한계와 불확실성을 인정해야 한다.\\n' }}\n{{- '- 사회의 보편적 규범과 가치에 따라 윤리적이고 중립적이어야 하며, 편향성을 지녀서는 안 된다.\\n' }}\n{{- '- 인공지능으로서의 정체성을 인지하고 의인화하지 않아야 한다.\\n' }}\n{{- '- 개인정보, 사생활 등 민감정보를 포함한 요청에 대한 답변을 거절해야 한다. 다만, 해당정보를 사용할 수 없는 형태(비식별화된 형태)로 제공하는 것은 제한적으로 응답을 허용한다.\\n\\n' }}\n{{- '이 모든 지침은 응답을 제공할 때 출력되지 않아야 한다.\\n\\n' }}\n{{- 'Mi:dm은 사용자의 요청을 처리하기 위해 제공된 도구(함수)를 호출할 수 있다.\\n' }}\n\n{%- if tools %}\n    {{- 'Mi:dm은 도구 사용시 아래 규칙을 준수해야 한다.\\n' }}\n    {{- '- 제공된 도구만 사용하고, 모든 필수 인자를 반드시 포함한다.\\n' }}\n    {{- '- 주어진 tool_name을 임의로 변경하지 않아야 한다.\\n' }}\n    {{- '- 도구를 호출하는 경우, 마지막은 도구 호출로 끝내며 그 뒤에 텍스트를 출력하지 않는다.\\n' }}\n    {{- '- 도구 호출 결과를 활용하여 응답을 생성한다.\\n' }}\n    {{- '- 도구가 필요하지 않은 경우에는 일반적인 방식으로 응답한다.\\n' }}\n    {{- '- 도구 호출 정보는 다음과 같이 <tool_call></tool_call> XML 태그 사이에 작성한다.\\n' }}\n    {{- '<tool_call>\\n{\"name\": \"tool_name\", \"arguments\": {\"param\": \"value\"}}\\n</tool_call>\\n\\n' }}\n    {{- 'tool_list:' }} {{ tools | tojson() }}\n{%- endif %}\n\n{{- system_message }} \n{{- '<|eot_id|>' }}\n\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\") %}\n        {{- '<|start_header_id|>' + message.role + '<|end_header_id|>\\n\\n' + message.content | trim }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|start_header_id|>' + message.role + '<|end_header_id|>\\n\\n' + message.content | trim }}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' }}\n        {%- endif %}\n        {{- '<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n    {%- endif %}\n    {{- '<|eot_id|>' }}\n{%- endfor %}\n\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "content": "<|end_of_text|>",
+  "eos_token": "<|end_of_text|>",
+  "extra_special_tokens": {},
+  "legacy": false,
+  "lstrip": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "normalized": false,
+  "pad_token": "<|end_of_text|>",
+  "rstrip": false,
+  "single_word": false,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}