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## MiniCPM-V 2.6
> Archieve at: 2025-08-25
**MiniCPM-V 2.6** 是 MiniCPM-V 系列中最新、性能最佳的模型。该模型基于 SigLip-400M 和 Qwen2-7B 构建，共 8B 参数。与 MiniCPM-Llama3-V 2.5 相比，MiniCPM-V 2.6 性能提升显著，并引入了多图和视频理解的新功能。MiniCPM-V 2.6 的主要特点包括：
- 🔥 **领先的性能。**
MiniCPM-V 2.6 在最新版本 OpenCompass 榜单上（综合 8 个主流多模态评测基准）平均得分 65.2，**以8B量级的大小在单图理解方面超越了 GPT-4o mini、GPT-4V、Gemini 1.5 Pro 和 Claude 3.5 Sonnet 等主流商用闭源多模态大模型**。
- 🖼️ **多图理解和上下文学习。**
MiniCPM-V 2.6 还支持**多图对话和推理**。它在 Mantis-Eval、BLINK、Mathverse mv 和 Sciverse mv 等主流多图评测基准中取得了**最佳水平**，并展现出了优秀的上下文学习能力。
- 🎬 **视频理解。**
MiniCPM-V 2.6 还可以**接受视频输入**，进行对话和提供涵盖时序和空间信息的详细视频描述。模型在 有/无字幕 评测场景下的 Video-MME 表现均超过了 **GPT-4V、Claude 3.5 Sonnet 和 LLaVA-NeXT-Video-34B**等商用闭源模型。
- 💪 **强大的 OCR 能力及其他功能。**
MiniCPM-V 2.6 可以处理任意长宽比的图像，像素数可达 180 万（如 1344x1344）。在 OCRBench 上取得**最佳水平，超过 GPT-4o、GPT-4V 和 Gemini 1.5 Pro 等商用闭源模型**。基于最新的 [RLAIF-V](https://github.com/RLHF-V/RLAIF-V/) 和 [VisCPM](https://github.com/OpenBMB/VisCPM) 技术，其具备了**可信的多模态行为**，在 Object HalBench 上的幻觉率显著低于 GPT-4o 和 GPT-4V，并支持英语、中文、德语、法语、意大利语、韩语等**多种语言**。
- 🚀 **卓越的效率。**
除了对个人用户友好的模型大小，MiniCPM-V 2.6 还表现出**最先进的视觉 token 密度**（即每个视觉 token 编码的像素数量）。它**仅需 640 个 token 即可处理 180 万像素图像，比大多数模型少 75%**。这一特性优化了模型的推理速度、首 token 延迟、内存占用和功耗。因此，MiniCPM-V 2.6 可以支持 iPad 等终端设备上的高效**实时视频理解**。
- 💫 **易于使用。**
MiniCPM-V 2.6 可以通过多种方式轻松使用：(1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpmv-main/examples/llava/README-minicpmv2.6.md) 和 [ollama](https://github.com/OpenBMB/ollama/blob/minicpm-v2.6/examples/minicpm-v2.6/README.md) 支持在本地设备上进行高效的 CPU 推理，(2) [int4](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4) 和 [GGUF](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) 格式的量化模型，有 16 种尺寸，(3) [vLLM](#vllm-部署-) 支持高吞吐量和内存高效的推理，(4) 针对新领域和任务进行微调，(5) 使用 [Gradio](#本地-webui-demo-) 快速设置本地 WebUI 演示，(6) 在线[demo](http://120.92.209.146:8887/)即可体验。
### 性能评估 <!-- omit in toc -->
<div align="center">
<img src=assets/radar_final.png width=90% />
</div>
<details>
<summary>点击查看 OpenCompass, MME, MMVet, OCRBench, MMMU, MathVista, MMB, AI2D, TextVQA, DocVQA, HallusionBench, Object HalBench 上的单图评测结果详情。 </summary>
<div align="center">
<table style="margin: 0px auto;">
<thead>
<tr>
<th align="left">Model</th>
<th>Size</th>
<th>Token Density<sup>+</sup></th>
<th>OpenCompass</th>
<th>MME</th>
<th>MMVet</th>
<th>OCRBench</th>
<th>MMMU val</th>
<th>MathVista mini</th>
<th>MMB1.1 test</th>
<th>AI2D</th>
<th>TextVQA val</th>
<th>DocVQA test</th>
<th>HallusionBench</th>
<th>Object HalBench</th>
</tr>
</thead>
<tbody align="center">
<tr>
<td colspan="15" align="left"><strong>Proprietary</strong></td>
</tr>
<tr>
<td nowrap="nowrap" align="left">GPT-4o</td>
<td>-</td>
<td>1088</td>
<td>69.9</td>
<td>2328.7</td>
<td>69.1</td>
<td>736</td>
<td>69.2</td>
<td>61.3</td>
<td>82.2</td>
<td>84.6</td>
<td>-</td>
<td>92.8</td>
<td>55.0</td>
<td>17.6</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">Claude 3.5 Sonnet</td>
<td>-</td>
<td>750</td>
<td>67.9</td>
<td>1920.0</td>
<td>66.0</td>
<td>788</td>
<td>65.9</td>
<td>61.6</td>
<td>78.5</td>
<td>80.2</td>
<td>-</td>
<td>95.2</td>
<td>49.9</td>
<td>13.8</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">Gemini 1.5 Pro</td>
<td>-</td>
<td>-</td>
<td>64.4</td>
<td>2110.6</td>
<td>64.0</td>
<td>754</td>
<td>60.6</td>
<td>57.7</td>
<td>73.9</td>
<td>79.1</td>
<td>73.5</td>
<td>86.5</td>
<td>45.6</td>
<td>-</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">GPT-4o mini</td>
<td>-</td>
<td>1088</td>
<td>64.1</td>
<td>2003.4</td>
<td>66.9</td>
<td>785</td>
<td>60.0</td>
<td>52.4</td>
<td>76.0</td>
<td>77.8</td>
<td>-</td>
<td>-</td>
<td>46.1</td>
<td>12.4</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">GPT-4V</td>
<td>-</td>
<td>1088</td>
<td>63.5</td>
<td>2070.2</td>
<td>67.5</td>
<td>656</td>
<td>61.7</td>
<td>54.7</td>
<td>79.8</td>
<td>78.6</td>
<td>78.0</td>
<td>87.2</td>
<td>43.9</td>
<td>14.2</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">Step-1V</td>
<td>-</td>
<td>-</td>
<td>59.5</td>
<td>2206.4</td>
<td>63.3</td>
<td>625</td>
<td>49.9</td>
<td>44.8</td>
<td>78.0</td>
<td>79.2</td>
<td>71.6</td>
<td>-</td>
<td>48.4</td>
<td>-</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">Qwen-VL-Max</td>
<td>-</td>
<td>784</td>
<td>58.3</td>
<td>2281.7</td>
<td>61.8</td>
<td>684</td>
<td>52.0</td>
<td>43.4</td>
<td>74.6</td>
<td>75.7</td>
<td>79.5</td>
<td>93.1</td>
<td>41.2</td>
<td>13.4</td>
</tr>
<tr>
<td colspan="15" align="left"><strong>Open-source</strong></td>
</tr>
<tr>
<td nowrap="nowrap" align="left">LLaVA-NeXT-Yi-34B</td>
<td>34B</td>
<td>157</td>
<td>55.0</td>
<td>2006.5</td>
<td>50.7</td>
<td>574</td>
<td>48.8</td>
<td>40.4</td>
<td>77.8</td>
<td>78.9</td>
<td>69.3</td>
<td>-</td>
<td>34.8</td>
<td>12.6</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">Mini-Gemini-HD-34B</td>
<td>34B</td>
<td>157</td>
<td>-</td>
<td>2141</td>
<td>59.3</td>
<td>518</td>
<td>48.0</td>
<td>43.3</td>
<td>-</td>
<td>80.5</td>
<td>74.1</td>
<td>78.9</td>
<td>-</td>
<td>-</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">Cambrian-34B</td>
<td>34B</td>
<td>1820</td>
<td>58.3</td>
<td>2049.9</td>
<td>53.2</td>
<td>591</td>
<td>50.4</td>
<td>50.3</td>
<td>77.8</td>
<td>79.5</td>
<td>76.7</td>
<td>75.5</td>
<td>41.6</td>
<td>14.7</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">GLM-4V-9B</td>
<td>13B</td>
<td>784</td>
<td>59.1</td>
<td>2018.8</td>
<td>58.0</td>
<td>776</td>
<td>46.9</td>
<td>51.1</td>
<td>67.9</td>
<td>71.2</td>
<td>-</td>
<td>-</td>
<td>45.0</td>
<td>-</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">InternVL2-8B</td>
<td>8B</td>
<td>706</td>
<td>64.1</td>
<td>2215.1</td>
<td>54.3</td>
<td>794</td>
<td><strong>51.2</strong></td>
<td>58.3</td>
<td><strong>79.4</strong></td>
<td><strong>83.6</strong></td>
<td>77.4</td>
<td><strong>91.6</strong></td>
<td>45.0</td>
<td>21.3</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">MiniCPM-Llama-V 2.5</td>
<td>8B</td>
<td>1882</td>
<td>58.8</td>
<td>2024.6</td>
<td>52.8</td>
<td>725</td>
<td>45.8</td>
<td>54.3</td>
<td>72.0</td>
<td>78.4</td>
<td>76.6</td>
<td>84.8</td>
<td>42.4</td>
<td>10.3</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">MiniCPM-V 2.6</td>
<td>8B</td>
<td><strong>2822</strong></td>
<td><strong>65.2</strong></td>
<td><strong>2348.4</strong>*</td>
<td><strong>60.0</strong></td>
<td><strong>852</strong>*</td>
<td>49.8*</td>
<td><strong>60.6</strong></td>
<td>78.0</td>
<td>82.1</td>
<td><strong>80.1<strong></td>
<td>90.8</td>
<td><strong>48.1</strong>*</td>
<td><strong>8.2</strong></td>
</tr>
</tbody>
</table>
</div>
* 我们使用思维链提示词来评估这些基准。
<sup>+</sup> Token Density：每个视觉 token 在最大分辨率下编码的像素数，即最大分辨率下的像素数 / 视觉 token 数。
注意：闭源模型的 Token Density 由 API 收费方式估算得到。
</details>
<details>
<summary>点击查看 Mantis Eval, BLINK, Mathverse mv, Sciverse mv, MIRB 上的多图评测结果详情。</summary>
<div align="center">
<table style="margin: 0px auto;">
<thead>
<tr>
<th align="left">Model</th>
<th>Size</th>
<th>Mantis Eval</th>
<th>BLINK val</th>
<th>Mathverse mv</th>
<th>Sciverse mv</th>
<th>MIRB</th>
</tr>
</thead>
<tbody align="center">
<tr>
<td colspan="7" align="left"><strong>Proprietary</strong></td>
</tr>
<tr>
<td nowrap="nowrap" align="left">GPT-4V</td>
<td>-</td>
<td>62.7</td>
<td>54.6</td>
<td>60.3</td>
<td>66.9</td>
<td>53.1</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">LLaVA-NeXT-Interleave-14B</td>
<td>14B</td>
<td>66.4</td>
<td>52.6</td>
<td>32.7</td>
<td>30.2</td>
<td>-</td>
</tr>
<tr>
<td colspan="7" align="left"><strong>Open-source</strong></td>
</tr>
<tr>
<td nowrap="nowrap" align="left">Emu2-Chat</td>
<td>37B</td>
<td>37.8</td>
<td>36.2</td>
<td>-</td>
<td>27.2</td>
<td>-</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">CogVLM</td>
<td>17B</td>
<td>45.2</td>
<td>41.1</td>
<td>-</td>
<td>-</td>
<td>-</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">VPG-C</td>
<td>7B</td>
<td>52.4</td>
<td>43.1</td>
<td>24.3</td>
<td>23.1</td>
<td>-</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">VILA 8B</td>
<td>8B</td>
<td>51.2</td>
<td>39.3</td>
<td>-</td>
<td>36.5</td>
<td>-</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">InternLM-XComposer-2.5</td>
<td>8B</td>
<td>53.1*</td>
<td>48.9</td>
<td>32.1*</td>
<td>-</td>
<td>42.5</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">InternVL2-8B</td>
<td>8B</td>
<td>59.0*</td>
<td>50.9</td>
<td>30.5*</td>
<td>34.4*</td>
<td><strong>56.9*</strong></td>
</tr>
<tr>
<td nowrap="nowrap" align="left">MiniCPM-V 2.6</td>
<td>8B</td>
<td><strong>69.1</strong></td>
<td><strong>53.0</strong></td>
<td><strong>84.9</strong></td>
<td><strong>74.9</strong></td>
<td>53.8</td>
</tr>
</tbody>
</table>
</div>
* 正式开源模型权重的评测结果。
</details>
<details>
<summary>点击查看 Video-MME 和 Video-ChatGPT 上的视频评测结果详情。</summary>
<div align="center">
<table style="margin: 0px auto;">
<thead>
<tr>
<th align="left">Model</th>
<th>Size</th>
<th colspan="2">Video-MME</th>
<th colspan="5">Video-ChatGPT</th>
</tr>
<tr>
<th align="left"></th>
<th></th>
<th>w/o subs</th>
<th>w subs</th>
<th>Correctness</th>
<th>Detail</th>
<th>Context</th>
<th>Temporal</th>
<th>Consistency</th>
</tr>
</thead>
<tbody align="center">
<tr>
<td colspan="9" align="left"><strong>Proprietary</strong></td>
</tr>
<tr>
<td nowrap="nowrap" align="left">Claude 3.5 Sonnet</td>
<td>-</td>
<td>60.0</td>
<td>62.9</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>-</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">GPT-4V</td>
<td>-</td>
<td>59.9</td>
<td>63.3</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>-</td>
</tr>
<tr>
<td colspan="9" align="left"><strong>Open-source</strong></td>
</tr>
<tr>
<td nowrap="nowrap" align="left">LLaVA-NeXT-7B</td>
<td>7B</td>
<td>-</td>
<td>-</td>
<td>3.39</td>
<td>3.29</td>
<td>3.92</td>
<td>2.60</td>
<td>3.12</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">LLaVA-NeXT-34B</td>
<td>34B</td>
<td>-</td>
<td>-</td>
<td>3.29</td>
<td>3.23</td>
<td>3.83</td>
<td>2.51</td>
<td>3.47</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">CogVLM2-Video</td>
<td>12B</td>
<td>-</td>
<td>-</td>
<td>3.49</td>
<td><strong>3.46</strong></td>
<td>3.23</td>
<td><strong>2.98</strong></td>
<td><strong>3.64</strong></td>
</tr>
<tr>
<td nowrap="nowrap" align="left">LongVA</td>
<td>7B</td>
<td>52.4</td>
<td>54.3</td>
<td>3.05</td>
<td>3.09</td>
<td>3.77</td>
<td>2.44</td>
<td><strong>3.64</strong></td>
</tr>
<tr>
<td nowrap="nowrap" align="left">InternVL2-8B</td>
<td>8B</td>
<td>54.0</td>
<td>56.9</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>-</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">InternLM-XComposer-2.5</td>
<td>8B</td>
<td>55.8</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>-</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">LLaVA-NeXT-Video</td>
<td>32B</td>
<td>60.2</td>
<td>63.0</td>
<td>3.48</td>
<td>3.37</td>
<td><strong>3.95</strong></td>
<td>2.64</td>
<td>3.28</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">MiniCPM-V 2.6</td>
<td>8B</td>
<td><strong>60.9</strong></td>
<td><strong>63.6</strong></td>
<td><strong>3.59</strong></td>
<td>3.28</td>
<td>3.93</td>
<td>2.73</td>
<td>3.62</td>
</tr>
</tbody>
</table>
</div>
</details>
<details>
<summary>点击查看 TextVQA, VizWiz, VQAv2, OK-VQA上的少样本评测结果详情。</summary>
<div align="center">
<table style="margin: 0px auto;">
<thead>
<tr>
<th align="left">Model</th>
<th>Size</th>
<th>Shot</th>
<th>TextVQA val</th>
<th>VizWiz test-dev</th>
<th>VQAv2 test-dev</th>
<th>OK-VQA val</th>
</tr>
</thead>
<tbody align="center">
<tr>
<td align="left" nowrap="nowrap" rowspan="3">Flamingo</td>
<td rowspan="3">80B</td>
<td>0*</td>
<td>35.0</td>
<td>31.6</td>
<td>56.3</td>
<td>40.6</td>
</tr>
<tr>
<td>4</td>
<td>36.5</td>
<td>39.6</td>
<td>63.1</td>
<td><strong>57.4</strong></td>
</tr>
<tr>
<td>8</td>
<td>37.3</td>
<td>44.8</td>
<td>65.6</td>
<td>57.5</td>
</tr>
<tr>
<td align="left" nowrap="nowrap" rowspan="3">IDEFICS</td>
<td rowspan="3">80B</td>
<td>0*</td>
<td>30.9</td>
<td>36.0</td>
<td>60.0</td>
<td>45.2</td>
</tr>
<tr>
<td>4</td>
<td>34.3</td>
<td>40.4</td>
<td>63.6</td>
<td>52.4</td>
</tr>
<tr>
<td>8</td>
<td>35.7</td>
<td>46.1</td>
<td>64.8</td>
<td>55.1</td>
</tr>
<tr>
<td align="left" nowrap="nowrap" rowspan="3">OmniCorpus</td>
<td rowspan="3">7B</td>
<td>0*</td>
<td>43.0</td>
<td>49.8</td>
<td>63.2</td>
<td>45.5</td>
</tr>
<tr>
<td>4</td>
<td>45.4</td>
<td>51.3</td>
<td>64.5</td>
<td>46.5</td>
</tr>
<tr>
<td>8</td>
<td>45.6</td>
<td>52.2</td>
<td>64.7</td>
<td>46.6</td>
</tr>
<tr>
<td align="left" nowrap="nowrap" rowspan="3">Emu2</td>
<td rowspan="3">37B</td>
<td>0</td>
<td>26.4</td>
<td>40.4</td>
<td>33.5</td>
<td>26.7</td>
</tr>
<tr>
<td>4</td>
<td>48.2</td>
<td>54.6</td>
<td>67.0</td>
<td>53.2</td>
</tr>
<tr>
<td>8</td>
<td>49.3</td>
<td>54.7</td>
<td>67.8</td>
<td>54.1</td>
</tr>
<tr>
<td align="left" nowrap="nowrap" rowspan="2">MM1</td>
<td rowspan="2">30B</td>
<td>0</td>
<td>26.2</td>
<td>40.4</td>
<td>48.9</td>
<td>26.7</td>
</tr>
<tr>
<td>8</td>
<td>49.3</td>
<td>54.7</td>
<td><strong>70.9</strong></td>
<td>54.1</td>
</tr>
<tr>
<td align="left" nowrap="nowrap" rowspan="3">MiniCPM-V 2.6<sup>+</sup></td>
<td rowspan="3">8B</td>
<td>0</td>
<td>43.9</td>
<td>33.8</td>
<td>45.4</td>
<td>23.9</td>
</tr>
<tr>
<td>4</td>
<td>63.6</td>
<td>60.5</td>
<td>65.5</td>
<td>50.1</td>
</tr>
<tr>
<td>8</td>
<td><strong>64.6</strong></td>
<td><strong>63.4</strong></td>
<td>68.2</td>
<td>51.4</td>
</tr>
</tbody>
</table>
</div>
* 使用 Flamingo 方式 zero image shot 和 two additional text shots 评估零样本性能。
<sup>+</sup> 我们在没有进行监督微调 (SFT) 的情况下评估预训练的模型权重 (ckpt)。
</details>
### 典型示例 <!-- omit in toc -->
<div style="display: flex; flex-direction: column; align-items: center;">
<img src="../assets/minicpmv2_6/multi_img-bike.png" alt="Bike" style="margin-bottom: 5px;">
<img src="../assets/minicpmv2_6/multi_img-menu.png" alt="Menu" style="margin-bottom: 5px;">
<img src="../assets/minicpmv2_6/multi_img-code.png" alt="Code" style="margin-bottom: 5px;">
<img src="../assets/minicpmv2_6/ICL-Mem.png" alt="Mem" style="margin-bottom: 5px;">
<img src="../assets/minicpmv2_6/multiling-medal.png" alt="medal" style="margin-bottom: 10px;">
</div>
<details>
<summary>点击查看更多示例。</summary>
<div style="display: flex; flex-direction: column; align-items: center;">
<img src="../assets/minicpmv2_6/ICL-elec.png" alt="elec" style="margin-bottom: 5px;">
<img src="../assets/minicpmv2_6/multiling-olympic.png" alt="Menu" style="margin-bottom: 10px;">
</div>
</details>
我们将 MiniCPM-V 2.6 部署在iPad Pro上，并录制了以下演示视频。
<table align="center">
<p align="center">
<img src="../assets/gif_cases/ai.gif" width=32%/>
&nbsp;&nbsp;&nbsp;&nbsp;
<img src="../assets/gif_cases/beer.gif" width=32%/>
</p>
</table>
<table align="center">
<p align="center">
<video src="https://github.com/user-attachments/assets/21f4b818-ede1-4822-920e-91281725c830" width="360" /> </video>
<!-- <video src="https://github.com/user-attachments/assets/c835f757-206b-4d9c-8e36-70d67b453628" width="360" /> </video> -->
</p>
</table>
</details>
### 模型库
| 模型 | 设备 | 资源 | &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp; 简介 | 下载链接 |
|:--------------|:-:|:----------:|:-------------------|:---------------:|
| MiniCPM-V 2.6| GPU | 17 GB | 提供出色的端侧单图、多图、视频理解能力。 | [🤗](https://huggingface.co/openbmb/MiniCPM-V-2_6) &nbsp;&nbsp; [<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-V-2_6) |
| MiniCPM-V 2.6 gguf | CPU | 6 GB | gguf 版本，更低的内存占用和更高的推理效率。 | [🤗](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) &nbsp;&nbsp; [<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-V-2_6-gguf) |
| MiniCPM-V 2.6 int4 | GPU | 7 GB | int4量化版，更低显存占用。 | [🤗](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4) &nbsp;&nbsp; [<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-V-2_6-int4) |
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