Update README.md

README.md

@@ -10,11 +10,12 @@ base_model:
   
 <div align="center" style="display: flex; justify-content: center; align-items: center;">
   <img src="stepfun.svg" width="25" style="margin-right: 10px;"/>
-  <h1 style="margin: 0; border-bottom: none;">Step-3.5-Flash</h1>
+  <h1 style="margin: 0; border-bottom: none;">Step 3.5 Flash</h1>
 </div>
 
 [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20HF-StepFun/STEP3p5-preview)](https://huggingface.co/stepfun-ai/Step-3.5-Flash-FP8)
 [![ModelScope](https://img.shields.io/badge/ModelScope-StepFun/STEP3p5-preview)](https://huggingface.co/stepfun-ai/step3p5_preview/tree/main)
+[![Webpage](https://img.shields.io/badge/Webpage-Blog-blue)](https://static.stepfun.com/blog/step-3.5-flash/)
 [![Paper](https://img.shields.io/badge/Paper-Arxiv-red)](https://huggingface.co/stepfun-ai/Step-3.5-Flash-FP8)
 [![License](https://img.shields.io/badge/License-Apache%202.0-green)]()
 
@@ -22,7 +23,7 @@ base_model:
 
 ## 1. Introduction 
 
-**Step 3.5 Flash** is our most capable open-source foundation model, engineered to deliver frontier reasoning and agentic capabilities with exceptional efficiency. Built on a sparse Mixture of Experts (MoE) architecture, it selectively activates only 11B of its 196B parameters per token. This "intelligence density" allows it to rival the reasoning depth of top-tier proprietary models, while maintaining the agility required for real-time interaction.
+**Step 3.5 Flash** ([visit website](https://static.stepfun.com/blog/step-3.5-flash/)) is our most capable open-source foundation model, engineered to deliver frontier reasoning and agentic capabilities with exceptional efficiency. Built on a sparse Mixture of Experts (MoE) architecture, it selectively activates only 11B of its 196B parameters per token. This "intelligence density" allows it to rival the reasoning depth of top-tier proprietary models, while maintaining the agility required for real-time interaction.
 
 ## 2. Key Capabilities
 
@@ -213,7 +214,7 @@ vllm serve <MODEL_PATH_OR_HF_ID> \
 1. Install SGLang.
 ```bash
 # via Docker
-docker pull lmsysorg/sglang:latest
+docker pull lmsysorg/sglang:dev-pr-18084
 # or from source (pip)
 pip install "sglang[all] @ git+https://github.com/sgl-project/sglang.git"
 ```