[v0.11.0][Doc] Update doc (#3852)

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


Signed-off-by: hfadzxy <starmoon_zhang@163.com>

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).