Simplify documentation in README.md (#1851)

docs/backend.md

-## Backend: SGLang Runtime (SRT)
+# Backend: SGLang Runtime (SRT)
 The SGLang Runtime (SRT) is an efficient serving engine.
 
 ### Quick Start
@@ -79,6 +79,7 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct
 ```
 python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --chunked-prefill-size 4096
 ```
+- To enable the experimental overlapped scheduler, add `--enable-overlap-scheduler`. It overlaps CPU scheduler with GPU computation and can accelerate almost all workloads. This does not work for constrained decoding currenly.
 - To enable torch.compile acceleration, add `--enable-torch-compile`. It accelerates small models on small batch sizes. This does not work for FP8 currenly.
 - To enable torchao quantization, add `--torchao-config int4wo-128`. It supports various quantization strategies.
 - To enable fp8 weight quantization, add `--quantization fp8` on a fp16 checkpoint or directly load a fp8 checkpoint without specifying any arguments.
@@ -128,7 +129,7 @@ You can view the full example [here](https://github.com/sgl-project/sglang/tree/
 - Llama / Llama 2 / Llama 3 / Llama 3.1
 - Mistral / Mixtral / Mistral NeMo
 - Gemma / Gemma 2
-- Qwen / Qwen 2 / Qwen 2 MoE
+- Qwen / Qwen 2 / Qwen 2 MoE / Qwen 2 VL
 - DeepSeek / DeepSeek 2
 - OLMoE
 - [LLaVA-OneVision](https://llava-vl.github.io/blog/2024-08-05-llava-onevision/)
@@ -151,6 +152,7 @@ You can view the full example [here](https://github.com/sgl-project/sglang/tree/
 - MiniCPM / MiniCPM 3
 - XVERSE / XVERSE MoE
 - SmolLM
+- GLM-4
 
 **Embedding Models**
 
@@ -173,6 +175,17 @@ Launch [Qwen2-7B-Instruct](https://www.modelscope.cn/models/qwen/qwen2-7b-instru
 SGLANG_USE_MODELSCOPE=true python -m sglang.launch_server --model-path qwen/Qwen2-7B-Instruct --port 30000
 ```
 
+Or start it by docker.
+```bash
+docker run --gpus all \
+    -p 30000:30000 \
+    -v ~/.cache/modelscope:/root/.cache/modelscope \
+    --env "SGLANG_USE_MODELSCOPE=true" \
+    --ipc=host \
+    lmsysorg/sglang:latest \
+    python3 -m sglang.launch_server --model-path Qwen/Qwen2.5-7B-Instruct --host 0.0.0.0 --port 30000
+```
+  
 </details>
 
 #### Run Llama 3.1 405B

docs/frontend.md

-## Frontend: Structured Generation Language (SGLang)
+# Frontend: Structured Generation Language (SGLang)
 The frontend language can be used with local models or API models. It is an alternative to the OpenAI API. You may found it easier to use for complex prompting workflow.
 
 ### Quick Start
-The example below shows how to use sglang to answer a mulit-turn question.
+The example below shows how to use SGLang to answer a multi-turn question.
 
 #### Using Local Models
 First, launch a server with

docs/install.md

-## Install SGLang
+# Install
 
 You can install SGLang using any of the methods below.