[Doc] Refactor and init user story page (#1224)

### What this PR does / why we need it?
This PR refactor the user stories page:
- Move it to community
- Add initial info of LLaMA-Factory, Huggingface/trl, MindIE Turbo,
GPUStack, verl
- Add a new page for LLaMA-Factory

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
Preview locally

Signed-off-by: Yikun Jiang <yikunkero@gmail.com>

docs/source/community/user_stories/index.md

@@ -0,0 +1,19 @@
+# User Stories
+
+Read case studies on how users and developers solves 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.
+
+- [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.
+
+- [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).
+
+- [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).
+
+- [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).
+
+:::{toctree}
+:caption: More details
+:maxdepth: 1
+llamafactory
+:::

docs/source/community/user_stories/llamafactory.md

@@ -0,0 +1,19 @@
+# LLaMA-Factory
+
+**About / 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. 
+
+**The Business Challenge**
+
+LLaMA-Factory used transformers to perform inference on Ascend NPU, but the speed was slow.
+
+**Solving Challenges and 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.
+
+**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).

docs/source/index.md

@@ -66,11 +66,5 @@ developer_guide/evaluation/index
 :maxdepth: 1
 community/governance
 community/contributors
-:::
-
-% User stories about vLLM Ascend project
-:::{toctree}
-:caption: User Story
-:maxdepth: 1
-user_stories/index
+community/user_stories/index
 :::

docs/source/user_stories/example.md

@@ -1,15 +0,0 @@
-# xxx project uses Ascend vLLM, gain 200% performance enhancement of inference.
-
-## About / Introduction
-Draft content
-
-## The Business Challenge
-Our goal is to ...
-
-## Solving challenges with vLLM Ascend
-vLLM Ascend helped us ...
-
-## Benefits using vLLM Ascend
-
-## Learn more
-more info about this case

docs/source/user_stories/index.md

@@ -1,22 +0,0 @@
-# vLLM Ascend User Stories
-
-Read case studies on how users and developers solves real, everyday problems with vLLM Ascend
-
-:::{card} Example user story
-:link: ./example
-:link-type: doc
-
-xxx project uses Ascend vLLM, gain 200% performance enhancement of inference.
-
-+++
-
-**Tags**: vLLM, Ascend, Inference
-
-:::
-
-:::{toctree}
-:caption: Deployment
-:maxdepth: 1
-:hidden:
-example
-:::