diff --git a/README.md b/README.md
index 5138e0a455..0589d9bf5a 100644
--- a/README.md
+++ b/README.md
@@ -212,7 +212,7 @@ The default prebuilt package is compiled on **CUDA 12** since v0.3.0.
 For the GeForce RTX 50 series, please install the LMDeploy prebuilt package complied with **CUDA 12.8**
 
 ```shell
-export LMDEPLOY_VERSION=0.10.0
+export LMDEPLOY_VERSION=0.10.1
 export PYTHON_VERSION=310
 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
 ```
diff --git a/README_zh-CN.md b/README_zh-CN.md
index 179ffa466d..ddec4838b9 100644
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@@ -213,7 +213,7 @@ pip install lmdeploy
 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。
 
 ```shell
-export LMDEPLOY_VERSION=0.10.0
+export LMDEPLOY_VERSION=0.10.1
 export PYTHON_VERSION=310
 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
 ```
diff --git a/docs/en/get_started/installation.md b/docs/en/get_started/installation.md
index f0a9135907..90ed2a8a92 100644
--- a/docs/en/get_started/installation.md
+++ b/docs/en/get_started/installation.md
@@ -23,7 +23,7 @@ pip install lmdeploy
 The default prebuilt package is compiled on **CUDA 12**. If CUDA 11+ (>=11.3) is required, you can install lmdeploy by:
 
 ```shell
-export LMDEPLOY_VERSION=0.10.0
+export LMDEPLOY_VERSION=0.10.1
 export PYTHON_VERSION=310
 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
 ```
@@ -51,7 +51,7 @@ DISABLE_TURBOMIND=1 pip install git+https://github.com/InternLM/lmdeploy.git
 If you prefer a specific version instead of the `main` branch of LMDeploy, you can specify it in your command:
 
 ```shell
-pip install https://github.com/InternLM/lmdeploy/archive/refs/tags/v0.10.0.zip
+pip install https://github.com/InternLM/lmdeploy/archive/refs/tags/v0.10.1.zip
 ```
 
 If you want to build LMDeploy with support for Ascend, Cambricon, or MACA, install LMDeploy with the corresponding `LMDEPLOY_TARGET_DEVICE` environment variable.
diff --git a/docs/zh_cn/get_started/installation.md b/docs/zh_cn/get_started/installation.md
index 67b76a88b9..3ec71b0ccd 100644
--- a/docs/zh_cn/get_started/installation.md
+++ b/docs/zh_cn/get_started/installation.md
@@ -23,7 +23,7 @@ pip install lmdeploy
 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：
 
 ```shell
-export LMDEPLOY_VERSION=0.10.0
+export LMDEPLOY_VERSION=0.10.1
 export PYTHON_VERSION=310
 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
 ```
@@ -51,7 +51,7 @@ DISABLE_TURBOMIND=1 pip install git+https://github.com/InternLM/lmdeploy.git
 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：
 
 ```shell
-pip install https://github.com/InternLM/lmdeploy/archive/refs/tags/v0.10.0.zip
+pip install https://github.com/InternLM/lmdeploy/archive/refs/tags/v0.10.1.zip
 ```
 
 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。
diff --git a/lmdeploy/version.py b/lmdeploy/version.py
index 9f36352280..63a6d62d3d 100644
--- a/lmdeploy/version.py
+++ b/lmdeploy/version.py
@@ -1,7 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from typing import Tuple
 
-__version__ = '0.10.0'
+__version__ = '0.10.1'
 short_version = __version__