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konantech
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Konan-LLM-OND

Text Generation
Transformers
Safetensors
Korean
English
qwen3
konanllm
conversational
text-generation-inference
Model card Files
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  1. .gitattributes +1 -0
  2. README.md +303 -3
  3. config.json +30 -0
  4. generation_config.json +8 -0
  5. model-00001-of-00002.safetensors +3 -0
  6. model-00002-of-00002.safetensors +3 -0
  7. model.safetensors.index.json +405 -0
  8. tokenizer.json +3 -0
  9. tokenizer_config.json +77 -0
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README.md CHANGED
@@ -1,3 +1,303 @@
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ pipeline_tag: text-generation
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+ base_model:
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+ - Qwen/Qwen3-4B-Base
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+ tags:
8
+ - konanllm
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+ language:
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+ - ko
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+ - en
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+ ---
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+ # Konan-LLM-OND
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+
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+ ## **Overview**
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+
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+ **Konan-LLM-OND**, a large language model from Konan Technology Inc., is based on [Qwen3-4B-Base](https://huggingface.co/Qwen/Qwen3-4B-Base). It has been specifically optimized for the Korean language through vocabulary expansion, continual pre-training, and instruction tuning to enhance performance and efficiency.
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+ * **Languages**: Primarily Korean, with support for English.
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+ * **Key Features:**
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+ * **Expanded Korean Vocabulary:** The model's vocabulary has been expanded with additional Korean tokens to improve tokenization efficiency. As a result, Konan-LLM-OND is approximately 30% more token-efficient with Korean input than Qwen3, leading to greater cost-effectiveness and processing speed.
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+ * **Continual Pre-training**: The model underwent continual pre-training on a large-scale Korean corpus using an expanded vocabulary. This process enhanced its fundamental understanding and text generation capabilities in Korean.
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+ * **Supervised Fine-Tuning (SFT):** The model was fine-tuned on a high-quality Korean instruction dataset to improve its ability to understand and execute a wide variety of real-world tasks.
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+
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+ ## Benchmark Results
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+
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+ #### **Model Performance (＜ 5B)**
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+
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+ <table border="1" style="border-collapse: collapse; width: 100%;">
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+ <thead>
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+ <tr>
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+ <th rowspan="2" style="text-align: center; padding: 8px;">Model</th>
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+ <th rowspan="2" style="text-align: center; padding: 8px;">Model size</th>
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+ <th colspan="3" style="text-align: center; padding: 8px;">Korean</th>
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+ <th colspan="3" style="text-align: center; padding: 8px;">English</th>
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+ </tr>
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+ <tr>
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+ <th style="text-align: center; padding: 8px;">KMMLU</th>
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+ <th style="text-align: center; padding: 8px;">HRM8K</th>
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+ <th style="text-align: center; padding: 8px;">Ko-IFEval</th>
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+ <th style="text-align: center; padding: 8px;">MMLU</th>
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+ <th style="text-align: center; padding: 8px;">GSM8K</th>
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+ <th style="text-align: center; padding: 8px;">IFEval</th>
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+ </tr>
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+ </thead>
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+ <tbody>
46
+ <tr>
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+ <td style="padding: 8px;"><strong>Konan-LLM-OND</strong></td>
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+ <td style="text-align: center; padding: 8px;">4.0B</td>
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+ <td style="text-align: center; padding: 8px;"><strong>50.6<strong></td>
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+ <td style="text-align: center; padding: 8px;"><strong>46.4<strong></td>
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+ <td style="text-align: center; padding: 8px;">68.4</td>
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+ <td style="text-align: center; padding: 8px;"><strong>68.8<strong></td>
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+ <td style="text-align: center; padding: 8px;"><strong>86.8<strong></td>
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+ <td style="text-align: center; padding: 8px;">73.3</td>
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+ </tr>
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+ <tr>
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+ <td style="padding: 8px;"><strong>EXAONE-3.5-2.4B-Instruct</strong></td>
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+ <td style="text-align: center; padding: 8px;">2.4B</td>
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+ <td style="text-align: center; padding: 8px;">44.2</td>
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+ <td style="text-align: center; padding: 8px;">31.8</td>
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+ <td style="text-align: center; padding: 8px;">60.5</td>
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+ <td style="text-align: center; padding: 8px;">59.1</td>
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+ <td style="text-align: center; padding: 8px;">81.5</td>
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+ <td style="text-align: center; padding: 8px;">77.7</td>
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+ </tr>
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+ <tr>
67
+ <td style="padding: 8px;"><strong>kanana-1.5-2.1b-instruct-2505</strong></td>
68
+ <td style="text-align: center; padding: 8px;">2.1B</td>
69
+ <td style="text-align: center; padding: 8px;">32.7</td>
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+ <td style="text-align: center; padding: 8px;">27.2</td>
71
+ <td style="text-align: center; padding: 8px;">56.0</td>
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+ <td style="text-align: center; padding: 8px;">52.9</td>
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+ <td style="text-align: center; padding: 8px;">68.8</td>
74
+ <td style="text-align: center; padding: 8px;">64.6</td>
75
+ </tr>
76
+ <tr>
77
+ <td style="padding: 8px;"><strong>Midm-2.0-Mini-Instruct</strong></td>
78
+ <td style="text-align: center; padding: 8px;">2.3B</td>
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+ <td style="text-align: center; padding: 8px;">42.4</td>
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+ <td style="text-align: center; padding: 8px;">36.2</td>
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+ <td style="text-align: center; padding: 8px;">66.8</td>
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+ <td style="text-align: center; padding: 8px;">57.4</td>
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+ <td style="text-align: center; padding: 8px;">74.8</td>
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+ <td style="text-align: center; padding: 8px;">68.3</td>
85
+ </tr>
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+ <tr>
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+ <td style="padding: 8px;"><strong>Qwen3-4B(w/o reasoning)</strong></td>
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+ <td style="text-align: center; padding: 8px;">4.0B</td>
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+ <td style="text-align: center; padding: 8px;">0.0(*)</td>
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+ <td style="text-align: center; padding: 8px;">37.5</td>
91
+ <td style="text-align: center; padding: 8px;">68.4</td>
92
+ <td style="text-align: center; padding: 8px;">29.4(*)</td>
93
+ <td style="text-align: center; padding: 8px;">83.9</td>
94
+ <td style="text-align: center; padding: 8px;"><strong>80.0<strong></td>
95
+ </tr>
96
+ <tr>
97
+ <td style="padding: 8px;"><strong>gemma-3-4b-it</strong></td>
98
+ <td style="text-align: center; padding: 8px;">4.3B</td>
99
+ <td style="text-align: center; padding: 8px;">38.7</td>
100
+ <td style="text-align: center; padding: 8px;">32.7</td>
101
+ <td style="text-align: center; padding: 8px;"><strong>69.2<strong></td>
102
+ <td style="text-align: center; padding: 8px;">59.1</td>
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+ <td style="text-align: center; padding: 8px;">82.2</td>
104
+ <td style="text-align: center; padding: 8px;">78.3</td>
105
+ </tr>
106
+ </tbody>
107
+ </table>
108
+
109
+ #### **Model Performance (≥ 7B)**
110
+
111
+ <table border="1" style="border-collapse: collapse; width: 100%;">
112
+ <thead>
113
+ <tr>
114
+ <th rowspan="2" style="text-align: center; padding: 8px;">Model</th>
115
+ <th rowspan="2" style="text-align: center; padding: 8px;">Model size</th>
116
+ <th colspan="3" style="text-align: center; padding: 8px;">Korean</th>
117
+ <th colspan="3" style="text-align: center; padding: 8px;">English</th>
118
+ </tr>
119
+ <tr>
120
+ <th style="text-align: center; padding: 8px;">KMMLU</th>
121
+ <th style="text-align: center; padding: 8px;">HRM8K</th>
122
+ <th style="text-align: center; padding: 8px;">Ko-IFEval</th>
123
+ <th style="text-align: center; padding: 8px;">MMLU</th>
124
+ <th style="text-align: center; padding: 8px;">GSM8K</th>
125
+ <th style="text-align: center; padding: 8px;">IFEval</th>
126
+ </tr>
127
+ </thead>
128
+ <tbody>
129
+ <tr>
130
+ <td style="padding: 8px;"><strong>Konan-LLM-OND</strong></td>
131
+ <td style="text-align: center; padding: 8px;">4.0B</td>
132
+ <td style="text-align: center; padding: 8px;">50.6</td>
133
+ <td style="text-align: center; padding: 8px;"><strong>46.4</strong></td>
134
+ <td style="text-align: center; padding: 8px;">68.4</td>
135
+ <td style="text-align: center; padding: 8px;">68.8</td>
136
+ <td style="text-align: center; padding: 8px;">86.8</td>
137
+ <td style="text-align: center; padding: 8px;">73.3</td>
138
+ </tr>
139
+ <tr>
140
+ <td style="padding: 8px;"><strong>A.X-4.0-Light</strong></td>
141
+ <td style="text-align: center; padding: 8px;">7.2B</td>
142
+ <td style="text-align: center; padding: 8px;"><strong>55.3</strong></td>
143
+ <td style="text-align: center; padding: 8px;">44.6</td>
144
+ <td style="text-align: center; padding: 8px;">71.5</td>
145
+ <td style="text-align: center; padding: 8px;"><strong>70.6</strong></td>
146
+ <td style="text-align: center; padding: 8px;">87.3</td>
147
+ <td style="text-align: center; padding: 8px;">81.3</td>
148
+ </tr>
149
+ <tr>
150
+ <td style="padding: 8px;"><strong>EXAONE-3.5-7.8B-Instruct</strong></td>
151
+ <td style="text-align: center; padding: 8px;">7.8B</td>
152
+ <td style="text-align: center; padding: 8px;">48.0</td>
153
+ <td style="text-align: center; padding: 8px;">39.3</td>
154
+ <td style="text-align: center; padding: 8px;">66.8</td>
155
+ <td style="text-align: center; padding: 8px;">66.8</td>
156
+ <td style="text-align: center; padding: 8px;"><strong>91.4</strong></td>
157
+ <td style="text-align: center; padding: 8px;">79.9</td>
158
+ </tr>
159
+ <tr>
160
+ <td style="padding: 8px;"><strong>kanana-1.5-8b-instruct-2505</strong></td>
161
+ <td style="text-align: center; padding: 8px;">8.0B</td>
162
+ <td style="text-align: center; padding: 8px;">40.4</td>
163
+ <td style="text-align: center; padding: 8px;">35.5</td>
164
+ <td style="text-align: center; padding: 8px;">71.1</td>
165
+ <td style="text-align: center; padding: 8px;">63.1</td>
166
+ <td style="text-align: center; padding: 8px;">79.3</td>
167
+ <td style="text-align: center; padding: 8px;">76.8</td>
168
+ </tr>
169
+ <tr>
170
+ <td style="padding: 8px;"><strong>Midm-2.0-Base-Instruct</strong></td>
171
+ <td style="text-align: center; padding: 8px;">11.5B</td>
172
+ <td style="text-align: center; padding: 8px;">54.2</td>
173
+ <td style="text-align: center; padding: 8px;">46.0</td>
174
+ <td style="text-align: center; padding: 8px;"><strong>75.0</strong></td>
175
+ <td style="text-align: center; padding: 8px;">70.2</td>
176
+ <td style="text-align: center; padding: 8px;">88.9</td>
177
+ <td style="text-align: center; padding: 8px;">79.7</td>
178
+ </tr>
179
+ <tr>
180
+ <td style="padding: 8px;"><strong>Qwen3-8B(w/o reasoning)</strong></td>
181
+ <td style="text-align: center; padding: 8px;">8.1B</td>
182
+ <td style="text-align: center; padding: 8px;">0.0(*)</td>
183
+ <td style="text-align: center; padding: 8px;">40.0</td>
184
+ <td style="text-align: center; padding: 8px;">70.9</td>
185
+ <td style="text-align: center; padding: 8px;">7.4(*)</td>
186
+ <td style="text-align: center; padding: 8px;">84.0</td>
187
+ <td style="text-align: center; padding: 8px;"><strong>82.8</strong></td>
188
+ </tr>
189
+ </tbody>
190
+ </table>
191
+
192
+ Note:
193
+ * The highest scores are shown in bold.
194
+ * (*) Qwen3 models often failed to follow the required answer format in the few-shot setting. As a result, the MMLU and KMMLU scores are markedly lower than expected and should be considered unreliable.
195
+
196
+ ## **Benchmark Setup**
197
+
198
+ All benchmarks were executed using the following standardized environment.
199
+
200
+ * **Evaluation Framework**: `lm-evaluation-harness v0.4.9`
201
+ * **Runtime & Hardware**: All models were served with `vLLM v0.9.1` on a single NVIDIA GPU.
202
+ * **Inference Mode**: For every benchmark, we invoked the `chat_completions` API, and scores were computed solely from the generated responses.
203
+
204
+ #### **Metric Adjustments**
205
+
206
+ * MMLU was evaluated following the KMMLU protocol.
207
+ * Ko-IFEval was evaluated using the original IFEval protocol, with the dataset sourced from [allganize/IFEval-Ko](https://huggingface.co/datasets/allganize/IFEval-Ko).
208
+
209
+ #### **Evaluation Protocol**
210
+
211
+ <table>
212
+ <thead>
213
+ <tr>
214
+ <th>Benchmark</th>
215
+ <th>Scoring Method</th>
216
+ <th>Few-shot</th>
217
+ </tr>
218
+ </thead>
219
+ <tbody>
220
+ <tr>
221
+ <td><strong>KMMLU</strong></td>
222
+ <td><code>exact_match</code></td>
223
+ <td>5-shot</td>
224
+ </tr>
225
+ <tr>
226
+ <td><strong>HRM8K</strong></td>
227
+ <td>mean of <code>hrm8k_gsm8k</code>, <code>hrm8k_ksm</code>, <code>hrm8k_math</code>, <code>hrm8k_mmmlu</code>, <code>hrm8k_omni_math</code></td>
228
+ <td>5-shot</td>
229
+ </tr>
230
+ <tr>
231
+ <td><strong>Ko-IFEval</strong></td>
232
+ <td>mean of <code>prompt_level_strict_acc</code>, <code>inst_level_strict_acc</code>, <code>prompt_level_loose_acc</code>, <code>inst_level_loose_acc</code></td>
233
+ <td>0-shot</td>
234
+ </tr>
235
+ <tr>
236
+ <td><strong>MMLU</strong></td>
237
+ <td><code>exact_match</code></td>
238
+ <td>5-shot</td>
239
+ </tr>
240
+ <tr>
241
+ <td><strong>GSM8K</strong></td>
242
+ <td><code>exact_match</code> &amp; <code>flexible-extract</code></td>
243
+ <td>5-shot</td>
244
+ </tr>
245
+ <tr>
246
+ <td><strong>IFEval</strong></td>
247
+ <td>mean of <code>prompt_level_strict_acc</code>, <code>inst_level_strict_acc</code>, <code>prompt_level_loose_acc</code>, <code>inst_level_loose_acc</code></td>
248
+ <td>0-shot</td>
249
+ </tr>
250
+ </tbody>
251
+ </table>
252
+
253
+ ## Quickstart
254
+
255
+ **Konan-LLM-OND** is supported in `transformers v4.52.0` and later.
256
+ ```bash
257
+ pip install transformers>=4.52.0
258
+ ```
259
+
260
+ The code example below shows you how to get the model to generate content based on given inputs.
261
+ ```python
262
+ import torch
263
+ from transformers import AutoTokenizer, AutoModelForCausalLM
264
+
265
+ model_name = "konantech/Konan-LLM-OND"
266
+ model = AutoModelForCausalLM.from_pretrained(
267
+ model_name,
268
+ torch_dtype=torch.bfloat16,
269
+ device_map="auto",
270
+ )
271
+ model.eval()
272
+ tokenizer = AutoTokenizer.from_pretrained(model_name)
273
+
274
+ messages = [
275
+ {"role": "system", "content": "You are a helpful assistant."},
276
+ {"role": "user", "content": "대한민국 수도는?"}
277
+ ]
278
+
279
+
280
+ input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
281
+
282
+ with torch.no_grad():
283
+ output = model.generate(
284
+ input_ids,
285
+ max_new_tokens=64,
286
+ do_sample=False,
287
+ )
288
+
289
+ len_input_prompt = len(input_ids[0])
290
+ response = tokenizer.decode(output[0][len_input_prompt:], skip_special_tokens=True)
291
+ print(response)
292
+ # 대한민국 수도는 서울입니다.
293
+ ```
294
+
295
+ ## Citation
296
+ ```
297
+ @misc{Konan-LLM-OND-2025,
298
+ author = {Konan Technology Inc.},
299
+ title = {Konan-LLM-OND},
300
+ year = {2025},
301
+ url = {https://huggingface.co/konantech/Konan-LLM-OND}
302
+ }
303
+ ```
config.json ADDED
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+ {
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+ "architectures": [
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+ "Qwen3ForCausalLM"
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+ ],
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
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+ "bos_token_id": 172728,
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+ "eos_token_id": 172730,
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+ "head_dim": 128,
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+ "hidden_act": "silu",
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+ "hidden_size": 2560,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 9728,
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+ "max_position_embeddings": 32768,
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+ "max_window_layers": 36,
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+ "model_type": "qwen3",
17
+ "num_attention_heads": 32,
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+ "num_hidden_layers": 36,
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+ "num_key_value_heads": 8,
20
+ "rms_norm_eps": 1e-06,
21
+ "rope_scaling": null,
22
+ "rope_theta": 1000000,
23
+ "sliding_window": null,
24
+ "tie_word_embeddings": true,
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+ "torch_dtype": "bfloat16",
26
+ "transformers_version": "4.52.0",
27
+ "use_cache": true,
28
+ "use_sliding_window": false,
29
+ "vocab_size": 172800
30
+ }
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+ "eos_token_id": [
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+ ],
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+ "transformers_version": "4.52.0"
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+ }
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66
+ "bos_token": null,
67
+ "chat_template": "{%- for message in messages -%}\n {%- if message['role'] == 'system' -%}\n {{ '<|im_start|>system\n' + message['content'] + '<|im_end|>\n' }}\n {%- elif message['role'] == 'user' -%}\n {{ '<|im_start|>user\n' + message['content'] + '<|im_end|>\n' }}\n {%- elif message['role'] == 'assistant' -%}\n {{ '<|im_start|>assistant\n' + message['content'] + '<|im_end|>\n' }}\n {%- endif -%}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n {{ '<|im_start|>assistant\n' }}\n{%- endif -%}",
68
+ "clean_up_tokenization_spaces": false,
69
+ "eos_token": "<|im_end|>",
70
+ "errors": "replace",
71
+ "extra_special_tokens": {},
72
+ "model_max_length": 131072,
73
+ "pad_token": "<|endoftext|>",
74
+ "split_special_tokens": false,
75
+ "tokenizer_class": "Qwen2Tokenizer",
76
+ "unk_token": null
77
+ }