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README-en.md
244
README-en.md
@ -14,7 +14,7 @@
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<a href="https://openbmb.vercel.app/" target="_blank">Technical Blog</a> |
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<a href="https://github.com/OpenBMB/OmniLMM/" target="_blank">Multi-modal Model OmniLMM</a> |
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<a href="https://luca.cn/" target="_blank">CPM-C 100B Model Trial</a> |
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Join our <a href="https://discord.gg/3cGQn9b3YM" target="_blank">discord</a> and <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">wechat</a>
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Join our <a href="https://discord.gg/3cGQn9b3YM" target="_blank">discord</a> and <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">WeChat</a>
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</p>
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MiniCPM is an End-Side LLM developed by ModelBest Inc. and TsinghuaNLP, with only 2.4B parameters excluding embeddings (2.7B in total).
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@ -60,7 +60,7 @@ We release all model parameters for research and limited commercial use.
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<p id="0"></p>
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## Update Log
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- 2024/04/11 We release [MiniCPM-V 2.0](https://huggingface.co/openbmb/MiniCPM-V-2.0), [MiniCPM-2B-128k](https://huggingface.co/openbmb/MiniCPM-2B-128k), [MiniCPM-MoE-8x2B](https://huggingface.co/openbmb/MiniCPM-MoE-8x2B) and [MiniCPM-1B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-1B-sft-bf16)!
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- 2024/04/11 We release [MiniCPM-V 2.0](https://huggingface.co/openbmb/MiniCPM-V-2.0), [MiniCPM-2B-128k](https://huggingface.co/openbmb/MiniCPM-2B-128k), [MiniCPM-MoE-8x2B](https://huggingface.co/openbmb/MiniCPM-MoE-8x2B) and [MiniCPM-1B](https://huggingface.co/openbmb/MiniCPM-1B-sft-bf16)!
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- 2024/03/16 Intermediate checkpoints were released [here](https://huggingface.co/openbmb/MiniCPM-2B-history)!
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- 2024/02/13 We support llama.cpp
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- 2024/02/09 We have included a [Community](#community) section in the README to encourage support for MiniCPM from the open-source community.
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@ -410,86 +410,236 @@ MBPP, instead of the hand-verified set.
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#### Multimodal evaluation
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<div align="left">
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<div align="center">
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<table style="margin: 0px auto;">
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<thead>
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<tr>
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<th align="left">Model</th>
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<th>Size</th>
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<th nowrap="nowrap" >Visual Tokens</th>
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<th>MME</th>
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<th nowrap="nowrap" >MMB dev (en)</th>
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<th nowrap="nowrap" >MMB dev (zh)</th>
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<th nowrap="nowrap" >MMMU val</th>
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<th nowrap="nowrap" >CMMMU val</th>
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<th>TextVQA val</th>
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<th>DocVQA test</th>
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<th>OCRBench</th>
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<th>OpenCompass</th>
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<th nowrap="nowrap" >MME</th>
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<th>MMB dev(en)</th>
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<th>MMB dev(zh)</th>
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<th>MMMU val</th>
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<th>MathVista</th>
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<th>LLaVA Bench</th>
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<th nowrap="nowrap">Object HalBench</th>
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</tr>
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</thead>
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<tbody align="center">
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<tr>
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<td align="left">LLaVA-Phi</td>
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<td align="right">3B</td>
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<td>576</td>
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<td>1335</td>
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<td>59.8</td>
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<td>- </td>
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<td colspan="12" align="left"><strong>Proprietary models</strong></td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">Gemini Pro Vision</td>
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<td>- </td>
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<td>74.6</td>
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<td>88.1</td>
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<td>680</td>
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<td>63.8</td>
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<td>2148.9</td>
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<td>75.2</td>
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<td>74.0</td>
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<td>48.9</td>
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<td>45.8</td>
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<td>79.9</td>
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<td>- </td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">MobileVLM</td>
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<td align="right">3B</td>
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<td>144</td>
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<td>1289</td>
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<td>59.6</td>
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<td>- </td>
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<td>- </td>
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<td nowrap="nowrap" align="left">GPT-4V</td>
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<td>- </td>
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<td>78.0</td>
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<td>88.4</td>
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<td>645</td>
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<td>63.2</td>
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<td>1771.5</td>
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<td>75.1</td>
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<td>75.0</td>
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<td>53.8</td>
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<td>47.8</td>
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<td>93.1</td>
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<td>86.4 / 92.7</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left" >Imp-v1</td>
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<td align="right">3B</td>
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<td>576</td>
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<td>1434</td>
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<td>66.5</td>
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<td>- </td>
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<td>- </td>
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<td colspan="12" align="left"><strong>Open-source models 6B~34B</strong></td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left" >Yi-VL-6B</td>
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<td align="right" >6.7B</td>
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<td>45.5*</td>
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<td>17.1*</td>
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<td>290</td>
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<td>49.3</td>
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<td>1915.1 </td>
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<td>68.6 </td>
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<td>68.3 </td>
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<td>40.3 </td>
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<td>28.8 </td>
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<td>51.9 </td>
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<td>- </td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left" >Qwen-VL-Chat</td>
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<td align="right" >9.6B</td>
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<td>256</td>
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<td>1487</td>
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<td>61.5</td>
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<td>62.6</td>
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<td>488 </td>
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<td>52.1 </td>
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<td>1860.0 </td>
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<td>60.6 </td>
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<td>56.7 </td>
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<td>35.9 </td>
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<td>30.7 </td>
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<td>37.0 </td>
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<td>33.8 </td>
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<td>67.7 </td>
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<td>56.2 / 80.0</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left" >CogVLM</td>
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<td align="right">17.4B </td>
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<td>1225</td>
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<td>1438 </td>
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<td>63.7 </td>
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<td>53.8 </td>
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<td>32.1 </td>
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<td nowrap="nowrap" align="left" >Yi-VL-34B</td>
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<td align="right" >34B</td>
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<td>43.4*</td>
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<td>16.9*</td>
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<td>290</td>
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<td>52.6 </td>
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<td>2050.2</td>
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<td>71.1</td>
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<td>71.4</td>
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<td>45.1</td>
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<td>30.7</td>
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<td>62.3</td>
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<td>- </td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left" ><b>MiniCPM-V(3B)</b></td>
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<td align="right">3B </td>
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<td>64</td>
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<td>1452 </td>
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<td>67.3 </td>
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<td>61.9 </td>
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<td nowrap="nowrap" align="left" >DeepSeek-VL-7B</td>
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<td align="right" >7.3B</td>
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<td>64.7*</td>
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<td>47.0* </td>
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<td>435</td>
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<td>55.6 </td>
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<td>1765.4 </td>
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<td>74.1 </td>
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<td>72.8 </td>
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<td>38.3 </td>
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<td>36.8</td>
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<td>77.8 </td>
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<td>- </td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left" >TextMonkey</td>
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<td align="right" >9.7B</td>
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<td>64.3</td>
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<td>66.7 </td>
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<td>558</td>
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<td>- </td>
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<td>- </td>
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<td>- </td>
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<td>- </td>
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<td>- </td>
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<td>-</td>
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<td>- </td>
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<td>- </td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left" >CogVLM-Chat</td>
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<td align="right" >17.4B</td>
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<td>70.4</td>
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<td>33.3*</td>
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<td>590 </td>
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<td>52.5 </td>
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<td>1736.6 </td>
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<td>63.7 </td>
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<td>53.8 </td>
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<td>37.3 </td>
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<td>34.7 </td>
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<td>32.1 </td>
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<td>73.9 </td>
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<td>73.6 / 87.4 </td>
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</tr>
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<tr>
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<td colspan="12" align="left"><strong>Open-source models 1B~3B </strong></td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left" >DeepSeek-VL-1.3B</td>
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<td align="right" >1.7B</td>
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<td>58.4*</td>
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<td>37.9*</td>
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<td>413</td>
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<td>46.0 </td>
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<td>1531.6 </td>
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<td>64.0 </td>
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<td>61.2 </td>
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<td>33.8 </td>
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<td>29.4 </td>
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<td>51.1 </td>
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<td>- </td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left" >MobileVLM V2</td>
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<td align="right" >3.1B</td>
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<td>57.5</td>
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<td>19.4*</td>
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<td>-</td>
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<td>-</td>
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<td>1440.5(P) </td>
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<td>63.2 </td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left" >Mini-Gemini</td>
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<td align="right" >2.2B</td>
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<td>56.2</td>
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<td>34.2*</td>
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<td>-</td>
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<td>-</td>
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<td>1653.0 </td>
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<td>59.8 </td>
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<td>- </td>
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<td>31.7 </td>
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<td>-</td>
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<td>- </td>
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<td>- </td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left" >MiniCPM-V</td>
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<td align="right" >2.8B </td>
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<td>60.6</td>
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<td>38.2 </td>
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<td>366</td>
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<td>47.6</td>
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<td>1650.2 </td>
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<td>67.9 </td>
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<td>65.3 </td>
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<td><strong>38.3</strong></td>
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<td>28.9</td>
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<td>51.3 </td>
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<td>78.4 / 88.5 </td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left" ><strong>MiniCPM-V 2.0</strong></td>
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<td align="right" >2.8B </td>
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<td><strong>74.1</strong></td>
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<td><strong>71.9</strong> </td>
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<td><strong>605</strong></td>
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<td><strong>55.0</strong></td>
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<td><strong>1808.6</strong> </td>
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<td><strong>69.6</strong> </td>
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<td><strong>68.1</strong> </td>
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<td>38.2 </td>
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<td><strong>38.7</strong></td>
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<td><strong>69.2</strong> </td>
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<td><strong>85.5 / 92.2 </strong></td>
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</tr>
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</tbody>
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</table>
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</div>
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* We evaluate the officially released checkpoint by ourselves.
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#### DPO evaluation
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246
README.md
246
README.md
@ -15,7 +15,7 @@
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<a href="https://openbmb.vercel.app/?category=Chinese+Blog" target="_blank">MiniCPM 技术博客</a> |
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<a href="https://github.com/OpenBMB/OmniLMM/" target="_blank">OmniLMM 多模态模型</a> |
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<a href="https://luca.cn/" target="_blank">CPM-C 千亿模型试用</a> |
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加入我们的 <a href="https://discord.gg/3cGQn9b3YM" target="_blank">discord</a> 和 <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">wechat</a>
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加入我们的 <a href="https://discord.gg/3cGQn9b3YM" target="_blank">discord</a> 和 <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">微信群</a>
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</p>
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@ -61,10 +61,10 @@ MiniCPM 是面壁智能与清华大学自然语言处理实验室共同开源的
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<p id="0"></p>
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## 更新日志
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- 2024/04/11 开源[MiniCPM-V-2.0](https://huggingface.co/openbmb/MiniCPM-V-2.0)、[MiniCPM-2B-128k](https://huggingface.co/openbmb/MiniCPM-2B-128k)、[MiniCPM-MoE-8x2B](https://huggingface.co/openbmb/MiniCPM-MoE-8x2B)和[MiniCPM-1B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-1B-sft-bf16)！
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- 2024/04/11 开源[MiniCPM-V-2.0](https://huggingface.co/openbmb/MiniCPM-V-2.0)、[MiniCPM-2B-128k](https://huggingface.co/openbmb/MiniCPM-2B-128k)、[MiniCPM-MoE-8x2B](https://huggingface.co/openbmb/MiniCPM-MoE-8x2B)和[MiniCPM-1B](https://huggingface.co/openbmb/MiniCPM-1B-sft-bf16)！
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- 2024/03/16 MiniCPM-2B 的30余个中间检查点开放了！[huggingface链接](https://huggingface.co/openbmb/MiniCPM-2B-history)
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- 2024/02/13 支持了llama.cpp
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- 2024/02/09 我们在readme里加入了一个[开源社区](#community)章节，用来收集开源社区对MiniCPM的支持案例。
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- 2024/02/09 我们在README里加入了一个[开源社区](#community)章节，用来收集开源社区对MiniCPM的支持案例。
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- 2024/02/08 我们更新了[llama-format的模型权重](#llamaformat)，方便大家更加快捷地使用我们的模型。
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- 2024/02/01 初始发布。
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@ -437,86 +437,236 @@ print(model.response("<用户>山东省最高的山是哪座山, 它比黄山高
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#### 多模态模型评测
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<div align="left">
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<div align="center">
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<table style="margin: 0px auto;">
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<thead>
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<tr>
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<th align="left">Model</th>
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<th>Size</th>
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<th nowrap="nowrap" >Visual Tokens</th>
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<th>MME</th>
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<th nowrap="nowrap" >MMB dev (en)</th>
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<th nowrap="nowrap" >MMB dev (zh)</th>
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<th nowrap="nowrap" >MMMU val</th>
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<th nowrap="nowrap" >CMMMU val</th>
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<th>TextVQA val</th>
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<th>DocVQA test</th>
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<th>OCRBench</th>
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<th>OpenCompass</th>
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<th nowrap="nowrap" >MME</th>
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<th>MMB dev(en)</th>
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<th>MMB dev(zh)</th>
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<th>MMMU val</th>
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<th>MathVista</th>
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<th>LLaVA Bench</th>
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<th nowrap="nowrap">Object HalBench</th>
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</tr>
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</thead>
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<tbody align="center">
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<tr>
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<td align="left">LLaVA-Phi</td>
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<td align="right">3B</td>
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<td>576</td>
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<td>1335</td>
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<td>59.8</td>
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<td>- </td>
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<td colspan="12" align="left"><strong>Proprietary models</strong></td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">Gemini Pro Vision</td>
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<td>- </td>
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<td>74.6</td>
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<td>88.1</td>
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<td>680</td>
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<td>63.8</td>
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<td>2148.9</td>
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<td>75.2</td>
|
||||
<td>74.0</td>
|
||||
<td>48.9</td>
|
||||
<td>45.8</td>
|
||||
<td>79.9</td>
|
||||
<td>- </td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left">MobileVLM</td>
|
||||
<td align="right">3B</td>
|
||||
<td>144</td>
|
||||
<td>1289</td>
|
||||
<td>59.6</td>
|
||||
<td>- </td>
|
||||
<td>- </td>
|
||||
<td nowrap="nowrap" align="left">GPT-4V</td>
|
||||
<td>- </td>
|
||||
<td>78.0</td>
|
||||
<td>88.4</td>
|
||||
<td>645</td>
|
||||
<td>63.2</td>
|
||||
<td>1771.5</td>
|
||||
<td>75.1</td>
|
||||
<td>75.0</td>
|
||||
<td>53.8</td>
|
||||
<td>47.8</td>
|
||||
<td>93.1</td>
|
||||
<td>86.4 / 92.7</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left" >Imp-v1</td>
|
||||
<td align="right">3B</td>
|
||||
<td>576</td>
|
||||
<td>1434</td>
|
||||
<td>66.5</td>
|
||||
<td>- </td>
|
||||
<td>- </td>
|
||||
<td colspan="12" align="left"><strong>Open-source models 6B~34B</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left" >Yi-VL-6B</td>
|
||||
<td align="right" >6.7B</td>
|
||||
<td>45.5*</td>
|
||||
<td>17.1*</td>
|
||||
<td>290</td>
|
||||
<td>49.3</td>
|
||||
<td>1915.1 </td>
|
||||
<td>68.6 </td>
|
||||
<td>68.3 </td>
|
||||
<td>40.3 </td>
|
||||
<td>28.8 </td>
|
||||
<td>51.9 </td>
|
||||
<td>- </td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left" >Qwen-VL-Chat</td>
|
||||
<td align="right" >9.6B</td>
|
||||
<td>256</td>
|
||||
<td>1487</td>
|
||||
<td>61.5</td>
|
||||
<td>62.6</td>
|
||||
<td>488 </td>
|
||||
<td>52.1 </td>
|
||||
<td>1860.0 </td>
|
||||
<td>60.6 </td>
|
||||
<td>56.7 </td>
|
||||
<td>35.9 </td>
|
||||
<td>30.7 </td>
|
||||
<td>37.0 </td>
|
||||
<td>33.8 </td>
|
||||
<td>67.7 </td>
|
||||
<td>56.2 / 80.0</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left" >CogVLM</td>
|
||||
<td align="right">17.4B </td>
|
||||
<td>1225</td>
|
||||
<td>1438 </td>
|
||||
<td>63.7 </td>
|
||||
<td>53.8 </td>
|
||||
<td>32.1 </td>
|
||||
<td nowrap="nowrap" align="left" >Yi-VL-34B</td>
|
||||
<td align="right" >34B</td>
|
||||
<td>43.4*</td>
|
||||
<td>16.9*</td>
|
||||
<td>290</td>
|
||||
<td>52.6 </td>
|
||||
<td>2050.2</td>
|
||||
<td>71.1</td>
|
||||
<td>71.4</td>
|
||||
<td>45.1</td>
|
||||
<td>30.7</td>
|
||||
<td>62.3</td>
|
||||
<td>- </td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left" ><b>MiniCPM-V(3B)</b></td>
|
||||
<td align="right">3B </td>
|
||||
<td>64</td>
|
||||
<td>1452 </td>
|
||||
<td>67.3 </td>
|
||||
<td>61.9 </td>
|
||||
<td nowrap="nowrap" align="left" >DeepSeek-VL-7B</td>
|
||||
<td align="right" >7.3B</td>
|
||||
<td>64.7*</td>
|
||||
<td>47.0* </td>
|
||||
<td>435</td>
|
||||
<td>55.6 </td>
|
||||
<td>1765.4 </td>
|
||||
<td>74.1 </td>
|
||||
<td>72.8 </td>
|
||||
<td>38.3 </td>
|
||||
<td>36.8</td>
|
||||
<td>77.8 </td>
|
||||
<td>- </td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left" >TextMonkey</td>
|
||||
<td align="right" >9.7B</td>
|
||||
<td>64.3</td>
|
||||
<td>66.7 </td>
|
||||
<td>558</td>
|
||||
<td>- </td>
|
||||
<td>- </td>
|
||||
<td>- </td>
|
||||
<td>- </td>
|
||||
<td>- </td>
|
||||
<td>-</td>
|
||||
<td>- </td>
|
||||
<td>- </td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left" >CogVLM-Chat</td>
|
||||
<td align="right" >17.4B</td>
|
||||
<td>70.4</td>
|
||||
<td>33.3*</td>
|
||||
<td>590 </td>
|
||||
<td>52.5 </td>
|
||||
<td>1736.6 </td>
|
||||
<td>63.7 </td>
|
||||
<td>53.8 </td>
|
||||
<td>37.3 </td>
|
||||
<td>34.7 </td>
|
||||
<td>32.1 </td>
|
||||
<td>73.9 </td>
|
||||
<td>73.6 / 87.4 </td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td colspan="12" align="left"><strong>Open-source models 1B~3B </strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left" >DeepSeek-VL-1.3B</td>
|
||||
<td align="right" >1.7B</td>
|
||||
<td>58.4*</td>
|
||||
<td>37.9*</td>
|
||||
<td>413</td>
|
||||
<td>46.0 </td>
|
||||
<td>1531.6 </td>
|
||||
<td>64.0 </td>
|
||||
<td>61.2 </td>
|
||||
<td>33.8 </td>
|
||||
<td>29.4 </td>
|
||||
<td>51.1 </td>
|
||||
<td>- </td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left" >MobileVLM V2</td>
|
||||
<td align="right" >3.1B</td>
|
||||
<td>57.5</td>
|
||||
<td>19.4*</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>1440.5(P) </td>
|
||||
<td>63.2 </td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left" >Mini-Gemini</td>
|
||||
<td align="right" >2.2B</td>
|
||||
<td>56.2</td>
|
||||
<td>34.2*</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>1653.0 </td>
|
||||
<td>59.8 </td>
|
||||
<td>- </td>
|
||||
<td>31.7 </td>
|
||||
<td>-</td>
|
||||
<td>- </td>
|
||||
<td>- </td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left" >MiniCPM-V</td>
|
||||
<td align="right" >2.8B </td>
|
||||
<td>60.6</td>
|
||||
<td>38.2 </td>
|
||||
<td>366</td>
|
||||
<td>47.6</td>
|
||||
<td>1650.2 </td>
|
||||
<td>67.9 </td>
|
||||
<td>65.3 </td>
|
||||
<td><strong>38.3</strong></td>
|
||||
<td>28.9</td>
|
||||
<td>51.3 </td>
|
||||
<td>78.4 / 88.5 </td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left" ><strong>MiniCPM-V 2.0</strong></td>
|
||||
<td align="right" >2.8B </td>
|
||||
<td><strong>74.1</strong></td>
|
||||
<td><strong>71.9</strong> </td>
|
||||
<td><strong>605</strong></td>
|
||||
<td><strong>55.0</strong></td>
|
||||
<td><strong>1808.6</strong> </td>
|
||||
<td><strong>69.6</strong> </td>
|
||||
<td><strong>68.1</strong> </td>
|
||||
<td>38.2 </td>
|
||||
<td><strong>38.7</strong></td>
|
||||
<td><strong>69.2</strong> </td>
|
||||
<td><strong>85.5 / 92.2 </strong></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
</div>
|
||||
* 我们自己评测了正式开源的模型权重。
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user