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bump version to v0.10.1 (#3989)
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‎README.md‎

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@@ -212,7 +212,7 @@ The default prebuilt package is compiled on **CUDA 12** since v0.3.0.
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For the GeForce RTX 50 series, please install the LMDeploy prebuilt package complied with **CUDA 12.8**
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```shell
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export LMDEPLOY_VERSION=0.10.0
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export LMDEPLOY_VERSION=0.10.1
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export PYTHON_VERSION=310
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pip install https://github.com/InternLM/lmdeploy/releases/download/v${LMDEPLOY_VERSION}/lmdeploy-${LMDEPLOY_VERSION}+cu128-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux2014_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu128
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```

‎README_zh-CN.md‎

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若使用 GeForce RTX 50 系列显卡，请安装基于 **CUDA 12.8** 编译的 LMDeploy 预编译包。
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```shell
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export LMDEPLOY_VERSION=0.10.0
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export LMDEPLOY_VERSION=0.10.1
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export PYTHON_VERSION=310
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pip install https://github.com/InternLM/lmdeploy/releases/download/v${LMDEPLOY_VERSION}/lmdeploy-${LMDEPLOY_VERSION}+cu128-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux2014_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu128
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```

‎docs/en/get_started/installation.md‎

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The default prebuilt package is compiled on **CUDA 12**. If CUDA 11+ (>=11.3) is required, you can install lmdeploy by:
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```shell
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export LMDEPLOY_VERSION=0.10.0
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export LMDEPLOY_VERSION=0.10.1
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export PYTHON_VERSION=310
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pip install https://github.com/InternLM/lmdeploy/releases/download/v${LMDEPLOY_VERSION}/lmdeploy-${LMDEPLOY_VERSION}+cu118-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux2014_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu118
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```
@@ -51,7 +51,7 @@ DISABLE_TURBOMIND=1 pip install git+https://github.com/InternLM/lmdeploy.git
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If you prefer a specific version instead of the `main` branch of LMDeploy, you can specify it in your command:
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```shell
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pip install https://github.com/InternLM/lmdeploy/archive/refs/tags/v0.10.0.zip
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pip install https://github.com/InternLM/lmdeploy/archive/refs/tags/v0.10.1.zip
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```
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If you want to build LMDeploy with support for Ascend, Cambricon, or MACA, install LMDeploy with the corresponding `LMDEPLOY_TARGET_DEVICE` environment variable.

‎docs/zh_cn/get_started/installation.md‎

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默认的预构建包是在 **CUDA 12** 上编译的。如果需要 CUDA 11+ (>=11.3)，你可以使用以下命令安装 lmdeploy：
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```shell
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export LMDEPLOY_VERSION=0.10.0
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export LMDEPLOY_VERSION=0.10.1
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export PYTHON_VERSION=310
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pip install https://github.com/InternLM/lmdeploy/releases/download/v${LMDEPLOY_VERSION}/lmdeploy-${LMDEPLOY_VERSION}+cu118-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux2014_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu118
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```
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如果您希望使用特定版本，而不是 LMDeploy 的 `main` 分支，可以在命令行中指定：
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```shell
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pip install https://github.com/InternLM/lmdeploy/archive/refs/tags/v0.10.0.zip
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pip install https://github.com/InternLM/lmdeploy/archive/refs/tags/v0.10.1.zip
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```
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如果您希望构建支持昇腾、寒武纪或沐熙的 LMDeploy，请使用相应的 `LMDEPLOY_TARGET_DEVICE` 环境变量进行安装。

‎lmdeploy/version.py‎

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# Copyright (c) OpenMMLab. All rights reserved.
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from typing import Tuple
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__version__ = '0.10.0'
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__version__ = '0.10.1'
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short_version = __version__
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