diff --git a/docs/source/tutorials/multi_node.md b/docs/source/tutorials/multi_node.md
index 5ef82ed..e4ca3c5 100644
--- a/docs/source/tutorials/multi_node.md
+++ b/docs/source/tutorials/multi_node.md
@@ -57,7 +57,7 @@ hccn_tool -i 0 -ping -g address 10.20.0.20
 ```
 
 ## Run with docker
-Assume you have two Atlas 800 A2(64G*8) nodes, and want to deploy the `deepseek-v3-w8a8` quantitative model across multi-node.
+Assume you have two Atlas 800 A2(64G*8) nodes, and want to deploy the `deepseek-v3.1-w8a8` quantitative model across multi-node.
 
 ```{code-block} bash
    :substitutions:
@@ -107,6 +107,7 @@ Before launch the inference server, ensure the following environment variables a
 nic_name="xxxx"
 local_ip="xxxx"
 
+export VLLM_USE_MODELSCOPE=True
 export HCCL_IF_IP=$local_ip
 export GLOO_SOCKET_IFNAME=$nic_name
 export TP_SOCKET_IFNAME=$nic_name
@@ -115,9 +116,9 @@ export OMP_PROC_BIND=false
 export OMP_NUM_THREADS=100
 export HCCL_BUFFSIZE=1024
 
-# The w8a8 weight can obtained from https://www.modelscope.cn/models/vllm-ascend/DeepSeek-V3-W8A8
+# The w8a8 weight can obtained from https://www.modelscope.cn/models/vllm-ascend/DeepSeek-V3.1-W8A8
 # If you want to the quantization manually, please refer to https://vllm-ascend.readthedocs.io/en/latest/user_guide/feature_guide/quantization.html
-vllm serve /root/.cache/ds_v3 \
+vllm serve vllm-ascend/DeepSeek-V3.1-W8A8 \
 --host 0.0.0.0 \
 --port 8004 \
 --data-parallel-size 4 \
@@ -126,7 +127,7 @@ vllm serve /root/.cache/ds_v3 \
 --data-parallel-rpc-port 13389 \
 --tensor-parallel-size 4 \
 --seed 1024 \
---served-model-name deepseek_v3 \
+--served-model-name deepseek_v3.1 \
 --enable-expert-parallel \
 --max-num-seqs 16 \
 --max-model-len 32768 \
@@ -146,6 +147,7 @@ vllm serve /root/.cache/ds_v3 \
 nic_name="xxx"
 local_ip="xxx"
 
+export VLLM_USE_MODELSCOPE=True
 export HCCL_IF_IP=$local_ip
 export GLOO_SOCKET_IFNAME=$nic_name
 export TP_SOCKET_IFNAME=$nic_name
@@ -155,7 +157,7 @@ export OMP_NUM_THREADS=100
 export VLLM_USE_V1=1
 export HCCL_BUFFSIZE=1024
 
-vllm serve /root/.cache/ds_v3 \
+vllm serve vllm-ascend/DeepSeek-V3.1-W8A8 \
 --host 0.0.0.0 \
 --port 8004 \
 --headless \
@@ -167,7 +169,7 @@ vllm serve /root/.cache/ds_v3 \
 --tensor-parallel-size 4 \
 --seed 1024 \
 --quantization ascend \
---served-model-name deepseek_v3 \
+--served-model-name deepseek_v3.1 \
 --max-num-seqs 16 \
 --max-model-len 32768 \
 --max-num-batched-tokens 4096 \
@@ -187,7 +189,7 @@ Once your server is started, you can query the model with input prompts:
 curl http://{ node0 ip:8004 }/v1/completions \
     -H "Content-Type: application/json" \
     -d '{
-        "model": "/root/.cache/ds_v3",
+        "model": "deepseek_v3.1",
         "prompt": "The future of AI is",
         "max_tokens": 50,
         "temperature": 0
@@ -198,7 +200,8 @@ curl http://{ node0 ip:8004 }/v1/completions \
 For details please refer to [benchmark](https://github.com/vllm-project/vllm-ascend/tree/main/benchmarks)
 
 ```shell
-vllm bench serve --model /root/.cache/ds_v3  --served-model-name deepseek_v3 \
+export VLLM_USE_MODELSCOPE=true
+vllm bench serve --model vllm-ascend/DeepSeek-V3.1-W8A8  --served-model-name deepseek_v3.1 \
 --dataset-name random --random-input-len 128 --random-output-len 128 \
 --num-prompts 200  --trust-remote-code --base-url "http://{ node0 ip }:8004" --request-rate 1
 ```