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@@ -4,7 +4,7 @@ MinerU supports installing extension modules on demand based on different needs
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## Common Scenarios
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### Core Functionality Installation
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-The `core` module is the core dependency of MinerU, containing all functional modules except `vllm`. Installing this module ensures the basic functionality of MinerU works properly.
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+The `core` module is the core dependency of MinerU, containing all functional modules except `vllm`/`lmdeploy`. Installing this module ensures the basic functionality of MinerU works properly.
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```bash
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uv pip install "mineru[core]"
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```
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@@ -12,18 +12,35 @@ uv pip install "mineru[core]"
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---
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### Using `vllm` to Accelerate VLM Model Inference
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-The `vllm` module provides acceleration support for VLM model inference, suitable for graphics cards with Turing architecture and later (8GB+ VRAM). Installing this module can significantly improve model inference speed.
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-In the configuration, `all` includes both `core` and `vllm` modules, so `mineru[all]` and `mineru[core,vllm]` are equivalent.
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+> [!NOTE]
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+> `vllm` and `lmdeploy` have nearly identical VLM inference acceleration effects and usage methods. You can choose one of them to install and use based on your actual needs, but it is not recommended to install both modules simultaneously to avoid potential dependency conflicts.
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+
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+The `vllm` module provides acceleration support for VLM model inference, suitable for graphics cards with Volta architecture and later (8GB+ VRAM). Installing this module can significantly improve model inference speed.
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+
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```bash
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-uv pip install "mineru[all]"
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+uv pip install "mineru[core,vllm]"
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```
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> [!TIP]
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> If exceptions occur during installation of the complete package including vllm, please refer to the [vllm official documentation](https://docs.vllm.ai/en/latest/getting_started/installation/index.html) to try to resolve the issue, or directly use the [Docker](./docker_deployment.md) deployment method.
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---
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-### Installing Lightweight Client to Connect to vllm-server
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-If you need to install a lightweight client on edge devices to connect to `vllm-server`, you can install the basic mineru package, which is very lightweight and suitable for devices with only CPU and network connectivity.
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+### Using `lmdeploy` to Accelerate VLM Model Inference
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+> [!NOTE]
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+> `vllm` and `lmdeploy` have nearly identical VLM inference acceleration effects and usage methods. You can choose one of them to install and use based on your actual needs, but it is not recommended to install both modules simultaneously to avoid potential dependency conflicts.
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+
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+The `lmdeploy` module provides acceleration support for VLM model inference, suitable for graphics cards with Volta architecture and later (8GB+ VRAM). Installing this module can significantly improve model inference speed.
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+
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+```bash
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+uv pip install "mineru[core,lmdeploy]"
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+```
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+> [!TIP]
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+> If exceptions occur during installation of the complete package including lmdeploy, please refer to the [lmdeploy official documentation](https://lmdeploy.readthedocs.io/en/latest/get_started/installation.html) to try to resolve the issue.
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+
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+---
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+
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+### Installing Lightweight Client to Connect to OpenAI-compatible servers
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+If you need to install a lightweight client on edge devices to connect to an OpenAI-compatible server for using VLM mode, you can install the basic mineru package, which is very lightweight and suitable for devices with only CPU and network connectivity.
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```bash
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uv pip install mineru
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```
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