diff --git a/docs/source/user_guide/feature_guide/layer_sharding.md b/docs/source/user_guide/feature_guide/layer_sharding.md
index 3d7bc160..10844bbb 100644
--- a/docs/source/user_guide/feature_guide/layer_sharding.md
+++ b/docs/source/user_guide/feature_guide/layer_sharding.md
@@ -1,8 +1,6 @@
----
-title: Layer Sharding Guide
----
+# Layer Sharding Linear Guide
 
-# Overview
+## Overview
 
 **Layer Shard Linear** is a memory-optimization feature designed for large language model (LLM) inference. It addresses the high memory pressure caused by **repeated linear operators across many layers** that share identical structure but have distinct weights.
 
@@ -19,14 +17,14 @@ This approach **preserves exact computational semantics** while **significantly
 
 ---
 
-## Flowchart
+### Flowchart
 ![layer shard](./images/layer_sharding.png)
 
 > **Figure.** Layer Shard Linear workflow: weights are sharded by layer across devices (top), and during forward execution (bottom), asynchronous broadcast pre-fetches the next layer's weight while the current layer computes—enabling zero-overhead weight loading.
 
 ---
 
-# Getting Started
+## Getting Started
 
 To enable **Layer Shard Linear**, specify the target linear layers using the `--additional-config` argument when launching your inference job. For example, to shard the `o_proj` and `q_b_proj` layers, use:
 
@@ -38,11 +36,11 @@ To enable **Layer Shard Linear**, specify the target linear layers using the `--
 
 ---
 
-# Supported Scenarios
+## Supported Scenarios
 
 This feature can be enabled in any scenario, but delivers the greatest benefit in the following cases:
 
-## FlashComm2-enabled
+### FlashComm2-enabled
 
 When using [FlashComm2](https://github.com/vllm-project/vllm-ascend/pull/4188), the full output projection (`o_proj`) matrix must be resident in memory for each layer. Layer sharding significantly reduces memory pressure by distributing these weights across devices.
 
@@ -57,7 +55,7 @@ vllm serve \
   }'
 ```
 
-## DSA-CP-enabled
+### DSA-CP-enabled
 
 With [DSA-CP](https://github.com/vllm-project/vllm-ascend/pull/4702), both `q_b_proj` and `o_proj` layers require large weight matrices to be stored per layer. Sharding these layers across NPUs helps fit extremely deep models (e.g., 61-layer architectures) into limited device memory.