[Docs] optimize and supplement docs system (#86)

Signed-off-by: Gaohan123 <gaohan19@huawei.com>

docs/README.md

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+# Welcome to vLLM-Omni
+
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" src="./source/logos/vllm-omni-logo-text-dark.png">
+    <img alt="vllm-omni" src="./source/logos/vllm-omni-logo-text-dark.png" width=55%>
+  </picture>
+</p>
+<h3 align="center">
+Easy, fast, and cheap omni-modality model serving for everyone
+</h3>
+
+<p style="text-align:center">
+<script async defer src="https://buttons.github.io/buttons.js"></script>
+<a class="github-button" href="https://github.com/vllm-project/vllm-omni" data-show-count="true" data-size="large" aria-label="Star">Star</a>
+<a class="github-button" href="https://github.com/vllm-project/vllm-omni/subscription" data-show-count="true" data-icon="octicon-eye" data-size="large" aria-label="Watch">Watch</a>
+<a class="github-button" href="https://github.com/vllm-project/vllm-omni/fork" data-show-count="true" data-icon="octicon-repo-forked" data-size="large" aria-label="Fork">Fork</a>
+</p>
+
+
+## About
+
+[vLLM](https://github.com/vllm-project/vllm) was originally designed to support large language models for text-based autoregressive generation tasks. vLLM-Omni extends its support for omni-modality model inference and serving:
+
+- **Omni-modality**: Text, image, video, and audio data processing
+- **Non-autoregressive Architectures**: extend the AR support of vLLM to Diffusion Transformers (DiT) and other parallel generation models
+- **Heterogeneous outputs**: from traditional text generation to multimodal outputs
+
+<p align="center">
+  <picture>
+    <img alt="vllm-omni" src="./source/architecture/omni-modality model architecture.png" width=55%>
+  </picture>
+</p>
+
+vLLM-omni is fast with:
+
+- Seamless AR support for efficient KV Cache management with vLLM
+- Pipelined stage execution overlapping
+- Fully disaggregation based on omniConnector and dynamic resource allocation across stages
+
+vLLM-omni is flexible and easy to use with:
+
+- Seamless integration with popular Hugging Face models
+- High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more
+- Tensor, pipeline, data and expert parallelism support for distributed inference
+- Streaming outputs
+- OpenAI-compatible API server
+
+vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
+
+- Multi-modality generation models (e.g. Qwen-image)
+- Omni-modality models (e.g. Qwen-omni)
+
+For more information, checkout the following: