bump version to v0.10.0

README.md

@@ -26,6 +26,7 @@ ______________________________________________________________________
 <details open>
 <summary><b>2025</b></summary>
 
+- \[2025/09\] TurboMind supports MXFP4 on NVIDIA GPUs starting from V100, achieving 1.5x the performmance of vLLM on H800 for openai gpt-oss models!
 - \[2025/06\] Comprehensive inference optimization for FP8 MoE Models
 - \[2025/06\] DeepSeek PD Disaggregation deployment is now supported through integration with [DLSlime](https://github.com/DeepLink-org/DLSlime) and [Mooncake](https://github.com/kvcache-ai/Mooncake). Huge thanks to both teams!
 - \[2025/04\] Enhance DeepSeek inference performance by integration deepseek-ai techniques: FlashMLA, DeepGemm, DeepEP, MicroBatch and eplb
@@ -149,6 +150,7 @@ LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by
   <li>Phi-3.5-MoE (16x3.8B)</li>
   <li>Phi-4-mini (3.8B)</li>
   <li>MiniCPM3 (4B)</li>
+  <li>gpt-oss (20B, 120B)</li>
 </ul>
 </td>
 <td>
@@ -204,7 +206,7 @@ conda activate lmdeploy
 pip install lmdeploy
 ```
 
-The default prebuilt package is compiled on **CUDA 12** since v0.3.0.
+The default prebuilt package is compiled on **CUDA 12.8** since v0.10.0.
 For more information on installing on CUDA 11+ platform, or for instructions on building from source, please refer to the [installation guide](docs/en/get_started/installation.md).
 
 ## Offline Batch Inference

README_ja.md

@@ -23,10 +23,6 @@ ______________________________________________________________________
 
 ## 最新ニュース 🎉
 
-<details open>
-<summary><b>2025</b></summary>
-</details>
-
 <details close>
 <summary><b>2024</b></summary>
 

README_zh-CN.md

@@ -27,6 +27,7 @@ ______________________________________________________________________
 <summary><b>2025</b></summary>
 </details>
 
+- 【2025年9月】TurboMind 引擎支持 MXFP4，适用于 NVIDIA V100 及以上 GPU。在 H800 上推理 openai gpt-oss 模型，性能可达 vLLM 的 1.5倍！
 - 【2025年6月】深度优化 FP8 MoE 模型推理
 - 【2025年6月】集成[DLSlime](https://github.com/DeepLink-org/DLSlime)和[Mooncake](https://github.com/kvcache-ai/Mooncake)，实现DeepSeek PD分离部署，向两个团队表示诚挚的感谢！
 - 【2025年4月】集成deepseek-ai组件FlashMLA、DeepGemm、DeepEP、MicroBatch、eplb等，提升DeepSeek推理性能
@@ -150,6 +151,7 @@ LMDeploy TurboMind 引擎拥有卓越的推理能力，在各种规模的模型
   <li>Phi-3.5-MoE (16x3.8B)</li>
   <li>Phi-4-mini (3.8B)</li>
   <li>MiniCPM3 (4B)</li>
+  <li>gpt-oss (20B, 120B)</li>
 </ul>
 </td>
 <td>
@@ -205,7 +207,7 @@ conda activate lmdeploy
 pip install lmdeploy
 ```
 
-自 v0.3.0 起，LMDeploy 预编译包默认基于 CUDA 12 编译。如果需要在 CUDA 11+ 下安装 LMDeploy，或者源码安装 LMDeploy，请参考[安装文档](docs/zh_cn/get_started/installation.md)
+自 v0.10.0 起，LMDeploy 预编译包默认基于 CUDA 12.8 编译。如果需要在 CUDA 11+ 下安装 LMDeploy，或者源码安装 LMDeploy，请参考[安装文档](docs/zh_cn/get_started/installation.md)
 
 ## 离线批处理
 

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=0.9.2
+export LMDEPLOY_VERSION=0.10.0
 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/v0.9.2.zip
+pip install https://github.com/InternLM/lmdeploy/archive/refs/tags/v0.10.0.zip
 ```
 
 If you want to build LMDeploy with support for Ascend, Cambricon, or MACA, install LMDeploy with the corresponding `LMDEPLOY_TARGET_DEVICE` environment variable.

docs/en/supported_models/supported_models.md

@@ -47,6 +47,7 @@ The following tables detail the models supported by LMDeploy's TurboMind engine
 |               GLM4               |        9B        | LLM  |    Yes    |   Yes   |   Yes   |  Yes  |
 |            CodeGeeX4             |        9B        | LLM  |    Yes    |   Yes   |   Yes   |   -   |
 |              Molmo               |     7B-D,72B     | MLLM |    Yes    |   Yes   |   Yes   |  No   |
+|             gpt-oss              |     20B,120B     | LLM  |    Yes    |   Yes   |   Yes   |  Yes  |
 
 "-" means not verified yet.
 

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=0.9.2
+export LMDEPLOY_VERSION=0.10.0
 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/v0.9.2.zip
+pip install https://github.com/InternLM/lmdeploy/archive/refs/tags/v0.10.0.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。

docs/zh_cn/supported_models/supported_models.md

@@ -47,6 +47,7 @@
 |               GLM4               |       9B       | LLM  |    Yes    |   Yes   |   Yes   |  Yes  |
 |            CodeGeeX4             |       9B       | LLM  |    Yes    |   Yes   |   Yes   |   -   |
 |              Molmo               |    7B-D,72B    | MLLM |    Yes    |   Yes   |   Yes   |  No   |
+|             gpt-oss              |    20B,120B    | LLM  |    Yes    |   Yes   |   Yes   |  Yes  |
 
 “-” 表示还没有验证。
 

lmdeploy/version.py

@@ -1,7 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from typing import Tuple
 
-__version__ = '0.9.2'
+__version__ = '0.10.0'
 short_version = __version__