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fix: update index.md and README files for improved clarity on lmdeploy-engine support

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myhloli
2025-11-17 11:24:03 +08:00
parent ad9521528e
commit 43881d5f66
4 changed files with 32 additions and 24 deletions
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4
README.md
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@@ -714,8 +714,8 @@ uv pip install -e .[core]
``` ```
> [!TIP] > [!TIP]
> `mineru[core]` includes all core features except `vLLM` acceleration, compatible with Windows / Linux / macOS systems, suitable for most users. > `mineru[core]` includes all core features except `vLLM`/`LMDeploy` acceleration, compatible with Windows / Linux / macOS systems, suitable for most users.
> If you need to use `vLLM` acceleration for VLM model inference or install a lightweight client on edge devices, please refer to the documentation [Extension Modules Installation Guide](https://opendatalab.github.io/MinerU/quick_start/extension_modules/). > If you need to use `vLLM`/`LMDeploy` acceleration for VLM model inference or install a lightweight client on edge devices, please refer to the documentation [Extension Modules Installation Guide](https://opendatalab.github.io/MinerU/quick_start/extension_modules/).
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4
README_zh-CN.md
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@@ -704,8 +704,8 @@ uv pip install -e .[core] -i https://mirrors.aliyun.com/pypi/simple
``` ```
> [!TIP] > [!TIP]
> `mineru[core]`包含除`vLLM`加速外的所有核心功能，兼容Windows / Linux / macOS系统，适合绝大多数用户。 > `mineru[core]`包含除`vLLM`/`LMDeploy`加速外的所有核心功能，兼容Windows / Linux / macOS系统，适合绝大多数用户。
> 如果您有使用`vLLM`加速VLM模型推理，或是在边缘设备安装轻量版client端等需求，可以参考文档[扩展模块安装指南](https://opendatalab.github.io/MinerU/zh/quick_start/extension_modules/)。 > 如果您需要使用`vLLM`/`LMDeploy`加速VLM模型推理，或是有在边缘设备安装轻量版client端等需求，可以参考文档[扩展模块安装指南](https://opendatalab.github.io/MinerU/zh/quick_start/extension_modules/)。
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24
docs/en/quick_start/index.md
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@@ -31,12 +31,13 @@ A WebUI developed based on Gradio, with a simple interface and only core parsing
<tr> <tr>
<th rowspan="2">Parsing Backend</th> <th rowspan="2">Parsing Backend</th>
<th rowspan="2">pipeline <br> (Accuracy<sup>1</sup> 82+)</th> <th rowspan="2">pipeline <br> (Accuracy<sup>1</sup> 82+)</th>
<th colspan="4" style="text-align:center;">vlm (Accuracy<sup>1</sup> 90+)</th> <th colspan="5">vlm (Accuracy<sup>1</sup> 90+)</th>
</tr> </tr>
<tr> <tr>
<th>transformers</th> <th>transformers</th>
<th>mlx-engine</th> <th>mlx-engine</th>
<th>vllm-engine / <br>vllm-async-engine</th> <th>vllm-engine / <br>vllm-async-engine</th>
<th>lmdeploy-engine</th>
<th>http-client</th> <th>http-client</th>
</tr> </tr>
</thead> </thead>
@@ -47,40 +48,42 @@ A WebUI developed based on Gradio, with a simple interface and only core parsing
<td>Good compatibility, <br>but slower</td> <td>Good compatibility, <br>but slower</td>
<td>Faster than transformers</td> <td>Faster than transformers</td>
<td>Fast, compatible with the vLLM ecosystem</td> <td>Fast, compatible with the vLLM ecosystem</td>
<td>Suitable for OpenAI-compatible servers<sup>5</sup></td> <td>Fast, compatible with the LMDeploy ecosystem</td>
<td>Suitable for OpenAI-compatible servers<sup>6</sup></td>
</tr> </tr>
<tr> <tr>
<th>Operating System</th> <th>Operating System</th>
<td colspan="2" style="text-align:center;">Linux<sup>2</sup> / Windows / macOS</td> <td colspan="2" style="text-align:center;">Linux<sup>2</sup> / Windows / macOS</td>
<td style="text-align:center;">macOS<sup>3</sup></td> <td style="text-align:center;">macOS<sup>3</sup></td>
<td style="text-align:center;">Linux<sup>2</sup> / Windows<sup>4</sup> </td> <td style="text-align:center;">Linux<sup>2</sup> / Windows<sup>4</sup> </td>
<td style="text-align:center;">Linux<sup>2</sup> / Windows<sup>5</sup> </td>
<td>Any</td> <td>Any</td>
</tr> </tr>
<tr> <tr>
<th>CPU inference support</th> <th>CPU inference support</th>
<td colspan="2" style="text-align:center;">✅</td> <td colspan="2" style="text-align:center;">✅</td>
<td colspan="2" style="text-align:center;">❌</td> <td colspan="3" style="text-align:center;">❌</td>
<td>Not required</td> <td>Not required</td>
</tr> </tr>
<tr> <tr>
<th>GPU Requirements</th><td colspan="2" style="text-align:center;">Volta or later architectures, 6 GB VRAM or more, or Apple Silicon</td> <th>GPU Requirements</th><td colspan="2" style="text-align:center;">Volta or later architectures, 6 GB VRAM or more, or Apple Silicon</td>
<td>Apple Silicon</td> <td>Apple Silicon</td>
<td>Volta or later architectures, 8 GB VRAM or more</td> <td colspan="2" style="text-align:center;">Volta or later architectures, 8 GB VRAM or more</td>
<td>Not required</td> <td>Not required</td>
</tr> </tr>
<tr> <tr>
<th>Memory Requirements</th> <th>Memory Requirements</th>
<td colspan="4" style="text-align:center;">Minimum 16 GB, 32 GB recommended</td> <td colspan="5" style="text-align:center;">Minimum 16 GB, 32 GB recommended</td>
<td>8 GB</td> <td>8 GB</td>
</tr> </tr>
<tr> <tr>
<th>Disk Space Requirements</th> <th>Disk Space Requirements</th>
<td colspan="4" style="text-align:center;">20 GB or more, SSD recommended</td> <td colspan="5" style="text-align:center;">20 GB or more, SSD recommended</td>
<td>2 GB</td> <td>2 GB</td>
</tr> </tr>
<tr> <tr>
<th>Python Version</th> <th>Python Version</th>
<td colspan="5" style="text-align:center;">3.10-3.13</td> <td colspan="6" style="text-align:center;">3.10-3.13</td>
</tr> </tr>
</tbody> </tbody>
</table> </table>
@@ -89,7 +92,8 @@ A WebUI developed based on Gradio, with a simple interface and only core parsing
<sup>2</sup> Linux supports only distributions released in 2019 or later. <sup>2</sup> Linux supports only distributions released in 2019 or later.
<sup>3</sup> MLX requires macOS 13.5 or later, recommended for use with version 14.0 or higher. <sup>3</sup> MLX requires macOS 13.5 or later, recommended for use with version 14.0 or higher.
<sup>4</sup> Windows vLLM support via WSL2(Windows Subsystem for Linux). <sup>4</sup> Windows vLLM support via WSL2(Windows Subsystem for Linux).
<sup>5</sup> Servers compatible with the OpenAI API, such as local or remote model services deployed via inference frameworks like `vLLM`, `SGLang`, or `LMDeploy`. <sup>5</sup> Windows LMDeploy can only use the `turbomind` backend, which is slightly slower than the `pytorch` backend. If performance is critical, it is recommended to run it via WSL2.
<sup>6</sup> Servers compatible with the OpenAI API, such as local or remote model services deployed via inference frameworks like `vLLM`, `SGLang`, or `LMDeploy`.
### Install MinerU ### Install MinerU
@@ -108,8 +112,8 @@ uv pip install -e .[core]
``` ```
> [!TIP] > [!TIP]
> `mineru[core]` includes all core features except `vllm` acceleration, compatible with Windows / Linux / macOS systems, suitable for most users. > `mineru[core]` includes all core features except `vLLM`/`LMDeploy` acceleration, compatible with Windows / Linux / macOS systems, suitable for most users.
> If you need to use `vllm` acceleration for VLM model inference or install a lightweight client on edge devices, please refer to the documentation [Extension Modules Installation Guide](./extension_modules.md). > If you need to use `vLLM`/`LMDeploy` acceleration for VLM model inference or install a lightweight client on edge devices, please refer to the documentation [Extension Modules Installation Guide](./extension_modules.md).
--- ---

24
docs/zh/quick_start/index.md
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@@ -31,12 +31,13 @@
<tr> <tr>
<th rowspan="2">解析后端</th> <th rowspan="2">解析后端</th>
<th rowspan="2">pipeline <br> (精度<sup>1</sup> 82+)</th> <th rowspan="2">pipeline <br> (精度<sup>1</sup> 82+)</th>
<th colspan="4" style="text-align:center;">vlm (精度<sup>1</sup> 90+)</th> <th colspan="5">vlm (精度<sup>1</sup> 90+)</th>
</tr> </tr>
<tr> <tr>
<th>transformers</th> <th>transformers</th>
<th>mlx-engine</th> <th>mlx-engine</th>
<th>vllm-engine / <br>vllm-async-engine</th> <th>vllm-engine / <br>vllm-async-engine</th>
<th>lmdeploy-engine</th>
<th>http-client</th> <th>http-client</th>
</tr> </tr>
</thead> </thead>
@@ -47,40 +48,42 @@
<td>兼容性好, 速度较慢</td> <td>兼容性好, 速度较慢</td>
<td>比transformers快</td> <td>比transformers快</td>
<td>速度快, 兼容vllm生态</td> <td>速度快, 兼容vllm生态</td>
<td>适用于OpenAI兼容服务器<sup>5</sup></td> <td>速度快, 兼容lmdeploy生态</td>
<td>适用于OpenAI兼容服务器<sup>6</sup></td>
</tr> </tr>
<tr> <tr>
<th>操作系统</th> <th>操作系统</th>
<td colspan="2" style="text-align:center;">Linux<sup>2</sup> / Windows / macOS</td> <td colspan="2" style="text-align:center;">Linux<sup>2</sup> / Windows / macOS</td>
<td style="text-align:center;">macOS<sup>3</sup></td> <td style="text-align:center;">macOS<sup>3</sup></td>
<td style="text-align:center;">Linux<sup>2</sup> / Windows<sup>4</sup> </td> <td style="text-align:center;">Linux<sup>2</sup> / Windows<sup>4</sup> </td>
<td style="text-align:center;">Linux<sup>2</sup> / Windows<sup>5</sup> </td>
<td>不限</td> <td>不限</td>
</tr> </tr>
<tr> <tr>
<th>CPU推理支持</th> <th>CPU推理支持</th>
<td colspan="2" style="text-align:center;">✅</td> <td colspan="2" style="text-align:center;">✅</td>
<td colspan="2" style="text-align:center;">❌</td> <td colspan="3" style="text-align:center;">❌</td>
<td >不需要</td> <td >不需要</td>
</tr> </tr>
<tr> <tr>
<th>GPU要求</th><td colspan="2" style="text-align:center;">Volta及以后架构, 6G显存以上或Apple Silicon</td> <th>GPU要求</th><td colspan="2" style="text-align:center;">Volta及以后架构, 6G显存以上或Apple Silicon</td>
<td>Apple Silicon</td> <td>Apple Silicon</td>
<td>Volta及以后架构, 8G显存以上</td> <td colspan="2" style="text-align:center;">Volta及以后架构, 8G显存以上</td>
<td>不需要</td> <td>不需要</td>
</tr> </tr>
<tr> <tr>
<th>内存要求</th> <th>内存要求</th>
<td colspan="4" style="text-align:center;">最低16GB以上, 推荐32GB以上</td> <td colspan="5" style="text-align:center;">最低16GB以上, 推荐32GB以上</td>
<td>8GB</td> <td>8GB</td>
</tr> </tr>
<tr> <tr>
<th>磁盘空间要求</th> <th>磁盘空间要求</th>
<td colspan="4" style="text-align:center;">20GB以上, 推荐使用SSD</td> <td colspan="5" style="text-align:center;">20GB以上, 推荐使用SSD</td>
<td>2GB</td> <td>2GB</td>
</tr> </tr>
<tr> <tr>
<th>python版本</th> <th>python版本</th>
<td colspan="5" style="text-align:center;">3.10-3.13</td> <td colspan="6" style="text-align:center;">3.10-3.13</td>
</tr> </tr>
</tbody> </tbody>
</table> </table>
@@ -89,7 +92,8 @@
<sup>2</sup> Linux仅支持2019年及以后发行版 <sup>2</sup> Linux仅支持2019年及以后发行版
<sup>3</sup> MLX需macOS 13.5及以上版本支持，推荐14.0以上版本使用 <sup>3</sup> MLX需macOS 13.5及以上版本支持，推荐14.0以上版本使用
<sup>4</sup> Windows vLLM通过WSL2(适用于 Linux 的 Windows 子系统)实现支持 <sup>4</sup> Windows vLLM通过WSL2(适用于 Linux 的 Windows 子系统)实现支持
<sup>5</sup> 兼容OpenAI API的服务器，如通过`vLLM`/`SGLang`/`LMDeploy`等推理框架部署的本地模型服务器或远程模型服务 <sup>5</sup> Windows LMDeploy只能使用`turbomind`后端，速度比`pytorch`后端稍慢，如对速度有要求建议通过WSL2运行
<sup>6</sup> 兼容OpenAI API的服务器，如通过`vLLM`/`SGLang`/`LMDeploy`等推理框架部署的本地模型服务器或远程模型服务
> [!TIP] > [!TIP]
> 除以上主流环境与平台外，我们也收录了一些社区用户反馈的其他平台支持情况，详情请参考[其他加速卡适配](https://opendatalab.github.io/MinerU/zh/usage/)。 > 除以上主流环境与平台外，我们也收录了一些社区用户反馈的其他平台支持情况，详情请参考[其他加速卡适配](https://opendatalab.github.io/MinerU/zh/usage/)。
@@ -113,8 +117,8 @@ uv pip install -e .[core] -i https://mirrors.aliyun.com/pypi/simple
``` ```
> [!TIP] > [!TIP]
> `mineru[core]`包含除`vllm`加速外的所有核心功能，兼容Windows / Linux / macOS系统，适合绝大多数用户。 > `mineru[core]`包含除`vLLM`/`LMDeploy`加速外的所有核心功能，兼容Windows / Linux / macOS系统，适合绝大多数用户。
> 如果您有使用`vllm`加速VLM模型推理，或是在边缘设备安装轻量版client端等需求，可以参考文档[扩展模块安装指南](./extension_modules.md)。 > 如果您需要使用`vLLM`/`LMDeploy`加速VLM模型推理，或是有在边缘设备安装轻量版client端等需求，可以参考文档[扩展模块安装指南](./extension_modules.md)。
--- ---