[Doc] Update doc (#3836)

### What this PR does / why we need it?

Update doc

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.11.0rc3
- vLLM main:
vllm-project/vllm@releases/v0.11.1

Signed-off-by: hfadzxy <[email protected]>

Author: zhangxinyuehfad

docs/source/community/contributors.md

@@ -17,7 +17,7 @@
 
 ## Contributors
 
-vLLM Ascend every release would not have been possible without the following contributors:
+Every release of vLLM Ascend would not have been possible without the following contributors:
 
 Updated on 2025-09-30:
 

docs/source/community/governance.md

@@ -1,48 +1,48 @@
 # Governance
 
 ## Mission
-As a vital component of vLLM, the vLLM Ascend project is dedicated to providing an easy, fast, and cheap LLM Serving for Everyone on Ascend NPU, and to actively contribute to the enrichment of vLLM.
+As a vital component of vLLM, the vLLM Ascend project is dedicated to providing an easy, fast, and cheap LLM Serving for everyone on Ascend NPUs and to actively contributing to the enrichment of vLLM.
 
 ## Principles
-vLLM Ascend follows the vLLM community's code of conduct：[vLLM - CODE OF CONDUCT](https://github.com/vllm-project/vllm/blob/main/CODE_OF_CONDUCT.md)
+vLLM Ascend follows the vLLM community's code of conduct: [vLLM - CODE OF CONDUCT](https://github.com/vllm-project/vllm/blob/main/CODE_OF_CONDUCT.md)
 
 ## Governance - Mechanics
 vLLM Ascend is an open-source project under the vLLM community, where the authority to appoint roles is ultimately determined by the vLLM community. It adopts a hierarchical technical governance structure.
 
 - Contributor:
 
-    **Responsibility:** Help new contributors on boarding, handle and respond to community questions, review RFCs, code
+    **Responsibility:** Help new contributors on boarding, handle and respond to community questions, review RFCs and code.
 
-    **Requirements:** Complete at least 1 contribution. Contributor is someone who consistently and actively participates in a project, included but not limited to issue/review/commits/community involvement.
+    **Requirements:** Complete at least 1 contribution. A contributor is someone who consistently and actively participates in a project, including but not limited to issue/review/commits/community involvement.
 
-    Contributors will be empowered [vllm-project/vllm-ascend](https://github.com/vllm-project/vllm-ascend) Github repo `Triage` permissions (`Can read and clone this repository. Can also manage issues and pull requests`) to help community developers collaborate more efficiently.
+    The contributor permissions are granted by the [vllm-project/vllm-ascend](https://github.com/vllm-project/vllm-ascend)'s repo `Triage` on GitHub, including repo read and clone, issue and PR management, facilitating efficient collaboration between community developers.
 
 - Maintainer:
 
-    **Responsibility:** Develop the project's vision and mission. Maintainers are responsible for driving the technical direction of the entire project and ensuring its overall success, possessing code merge permissions. They formulate the roadmap, review contributions from community members, continuously contribute code, and actively engage in community activities (such as regular meetings/events).
+    **Responsibility:** Develop the project's vision and mission. Maintainers are responsible for shaping the technical direction of the project and ensuring its long-term success. With code merge permissions, they lead roadmap planning, review community contributions, make ongoing code improvements, and actively participate in community engagement—such as regular meetings and events.
 
-    **Requirements:** Deep understanding of ‌vLLM‌ and ‌vLLM Ascend‌ codebases, with a commitment to sustained code contributions. Competency in ‌design/development/PR review workflows‌.
-    - **Review Quality‌:** Actively participate in community code reviews, ensuring high-quality code integration.
-    - **Quality Contribution‌:** Successfully develop and deliver at least one major feature while maintaining consistent high-quality contributions.
-    - **Community Involvement‌:** Actively address issues, respond to forum inquiries, participate in discussions, and engage in community-driven tasks.
+    **Requirements:** Deep understanding of ‌vLLM‌ and ‌vLLM Ascend‌ code bases, with a commitment to sustained code contributions and competency in ‌design, development, and PR review workflows‌.
 
-    Requires approval from existing Maintainers. The vLLM community has the final decision-making authority.
+    - **Review quality‌:** Actively participate in community code reviews, ensuring high-quality code integration.
+    - **Quality contribution‌:** Successfully develop and deliver at least one major feature while maintaining consistent high-quality contributions.
+    - **Community involvement‌:** Actively address issues, respond to forum inquiries, participate in discussions, and engage in community-driven tasks.
 
-    Maintainer will be empowered [vllm-project/vllm-ascend](https://github.com/vllm-project/vllm-ascend) Github repo write permissions (`Can read, clone, and push to this repository. Can also manage issues and pull requests`).
+The approval from existing Maintainers is required. The vLLM community has the final decision-making authority.
+Maintainers will be granted write access to the [vllm-project/vllm-ascend](https://github.com/vllm-project/vllm-ascend) GitHub repo. This includes permission to read, clone, and push to the repository, as well as manage issues and pull requests.
 
 ## Nominating and Removing Maintainers
 
 ### The Principles
 
-- Membership in vLLM Ascend is given to individuals on merit basis after they demonstrated strong expertise of the vLLM / vLLM Ascend through contributions, reviews and discussions.
+- Membership in vLLM Ascend is given to individuals on merit basis after they demonstrate their strong expertise in vLLM/vLLM Ascend through contributions, reviews, and discussions.
 
-- For membership in the maintainer group the individual has to demonstrate strong and continued alignment with the overall vLLM / vLLM Ascend principles.
+- For membership in the maintainer group, individuals have to demonstrate strong and continued alignment with the overall vLLM/vLLM Ascend principles.
 
-- Light criteria of moving module maintenance to ‘emeritus’ status if they don’t actively participate over long periods of time.
+- Maintainers who have been inactive for a long time may be transitioned to **emeritus** status under lenient criteria.
 
 - The membership is for an individual, not a company.
 
 ### Nomination and Removal
 
-- Nomination: Anyone can nominate someone to become a maintainer (include self-nominate). All existing maintainers are responsible for evaluating the nomination. The nominator should provide nominee's info around the strength of the candidate to be a maintainer, include but not limited to review quality, quality contribution, community involvement.
-- Removal: Anyone can nominate a person to be removed from maintainer position (include self-nominate). All existing maintainers are responsible for evaluating the nomination. The nominator should provide nominee's info, include but not limited to lack of activity, conflict with the overall direction and other information that makes them unfit to be a maintainer.
+- Nomination: Anyone can nominate a candidate to become a maintainer, including self-nominations. All existing maintainers are responsible for reviewing and evaluating each nomination. The nominator should provide relevant information about the nominee's qualifications—such as review quality, quality contribution, and community involvement—among other strengths.
+- Removal: Anyone may nominate an individual for removal from the maintainer role, including self-nominations. All current maintainers are responsible for reviewing and evaluating such nominations. The nominator should provide relevant information about the nominee—such as prolonged inactivity, misalignment with the project's overall direction, or other factors that may render them unsuitable for the maintainer position.

docs/source/community/user_stories/index.md

@@ -1,16 +1,16 @@
-# User Stories
+# User stories
 
-Read case studies on how users and developers solves real, everyday problems with vLLM Ascend
+Read case studies on how users and developers solve real, everyday problems with vLLM Ascend
 
-- [LLaMA-Factory](./llamafactory.md) is an easy-to-use and efficient platform for training and fine-tuning large language models, it supports vLLM Ascend to speed up inference since [LLaMA-Factory#7739](https://github.com/hiyouga/LLaMA-Factory/pull/7739), gain 2x performance enhancement of inference.
+- [LLaMA-Factory](./llamafactory.md) is an easy-to-use and efficient platform for training and fine-tuning large language models. It supports vLLM Ascend to speed up inference since [LLaMA-Factory#7739](https://github.com/hiyouga/LLaMA-Factory/pull/7739), gaining 2x performance enhancement in inference.
 
-- [Huggingface/trl](https://github.com/huggingface/trl) is a cutting-edge library designed for post-training foundation models using advanced techniques like SFT, PPO and DPO, it uses vLLM Ascend since [v0.17.0](https://github.com/huggingface/trl/releases/tag/v0.17.0) to support RLHF on Ascend NPU.
+- [Huggingface/trl](https://github.com/huggingface/trl) is a cutting-edge library designed for post-training foundation models using advanced techniques like SFT, PPO and DPO. It uses vLLM Ascend since [v0.17.0](https://github.com/huggingface/trl/releases/tag/v0.17.0) to support RLHF on Ascend NPUs.
 
-- [MindIE Turbo](https://pypi.org/project/mindie-turbo) is an LLM inference engine acceleration plug-in library developed by Huawei on Ascend hardware, which includes self-developed large language model optimization algorithms and optimizations related to the inference engine framework. It supports vLLM Ascend since [2.0rc1](https://www.hiascend.com/document/detail/zh/mindie/20RC1/AcceleratePlugin/turbodev/mindie-turbo-0001.html).
+- [MindIE Turbo](https://pypi.org/project/mindie-turbo) is an LLM inference engine acceleration plugin library developed by Huawei on Ascend hardware, which includes self-developed LLM optimization algorithms and optimizations related to the inference engine framework. It supports vLLM Ascend since [2.0rc1](https://www.hiascend.com/document/detail/zh/mindie/20RC1/AcceleratePlugin/turbodev/mindie-turbo-0001.html).
 
-- [GPUStack](https://github.com/gpustack/gpustack) is an open-source GPU cluster manager for running AI models. It supports vLLM Ascend since [v0.6.2](https://github.com/gpustack/gpustack/releases/tag/v0.6.2), see more GPUStack performance evaluation info on [link](https://mp.weixin.qq.com/s/pkytJVjcH9_OnffnsFGaew).
+- [GPUStack](https://github.com/gpustack/gpustack) is an open-source GPU cluster manager for running AI models. It supports vLLM Ascend since [v0.6.2](https://github.com/gpustack/gpustack/releases/tag/v0.6.2). See more GPUStack performance evaluation information at [this link](https://mp.weixin.qq.com/s/pkytJVjcH9_OnffnsFGaew).
 
-- [verl](https://github.com/volcengine/verl) is a flexible, efficient and production-ready RL training library for large language models (LLMs), uses vLLM Ascend since [v0.4.0](https://github.com/volcengine/verl/releases/tag/v0.4.0), see more info on [verl x Ascend Quickstart](https://verl.readthedocs.io/en/latest/ascend_tutorial/ascend_quick_start.html).
+- [verl](https://github.com/volcengine/verl) is a flexible, efficient, and production-ready RL training library for LLMs. It uses vLLM Ascend since [v0.4.0](https://github.com/volcengine/verl/releases/tag/v0.4.0). See more information on [verl x Ascend Quickstart](https://verl.readthedocs.io/en/latest/ascend_tutorial/ascend_quick_start.html).
 
 :::{toctree}
 :caption: More details

docs/source/community/user_stories/llamafactory.md

@@ -1,19 +1,19 @@
 # LLaMA-Factory
 
-**About / Introduction**
+**Introduction**
 
 [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) is an easy-to-use and efficient platform for training and fine-tuning large language models. With LLaMA-Factory, you can fine-tune hundreds of pre-trained models locally without writing any code.
 
-LLaMA-Facotory users need to evaluate and inference the model after fine-tuning the model.
+LLaMA-Facotory users need to evaluate and inference the model after fine-tuning.
 
-**The Business Challenge**
+**Business challenge**
 
-LLaMA-Factory used transformers to perform inference on Ascend NPU, but the speed was slow.
+LLaMA-Factory uses Transformers to perform inference on Ascend NPUs, but the speed is slow.
 
-**Solving Challenges and Benefits with vLLM Ascend**
+**Benefits with vLLM Ascend**
 
-With the joint efforts of LLaMA-Factory and vLLM Ascend ([LLaMA-Factory#7739](https://github.com/hiyouga/LLaMA-Factory/pull/7739)), the performance of LLaMA-Factory in the model inference stage has been significantly improved. According to the test results, the inference speed of LLaMA-Factory has been increased to 2x compared to the transformers version.
+With the joint efforts of LLaMA-Factory and vLLM Ascend ([LLaMA-Factory#7739](https://github.com/hiyouga/LLaMA-Factory/pull/7739)), LLaMA-Factory has achieved significant performance gains during model inference. Benchmark results show that its inference speed is now up to 2× faster compared to the Transformers implementation.
 
 **Learn more**
 
-See more about LLaMA-Factory and how it uses vLLM Ascend for inference on the Ascend NPU in the following documentation: [LLaMA-Factory Ascend NPU Inference](https://llamafactory.readthedocs.io/en/latest/advanced/npu_inference.html).
+See more details about LLaMA-Factory and how it uses vLLM Ascend for inference on Ascend NPUs in [LLaMA-Factory Ascend NPU Inference](https://llamafactory.readthedocs.io/en/latest/advanced/npu_inference.html).