[Doc][Misc] Correcting the document and uploading the model deployment template (#8287)

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### What this PR does / why we need it?
Correcting the document and uploading the model deployment template

### Does this PR introduce _any_ user-facing change?
no

### How was this patch tested?

---------

Signed-off-by: herizhen <1270637059@qq.com>
Signed-off-by: herizhen <59841270+herizhen@users.noreply.github.com>

docs/source/developer_guide/performance_and_debug/msprobe_guide.md

@@ -332,7 +332,6 @@ An L0 `dump.json` contains forward I/O for modules together with parameters. Usi
      "data_name": "Module.conv2.Conv2d.forward.0.parameters.bias.pt"
     }
    }
-  },
   }
  }
 }
@@ -389,7 +388,6 @@ An L1 `dump.json` records forward I/O for APIs. Using PyTorch's `relu` function
      "data_name": "Functional.relu.0.forward.output.0.pt"
     }
    ]
-  },
   }
  }
 }  

docs/source/developer_guide/performance_and_debug/optimization_and_tuning.md

@@ -111,7 +111,7 @@ sudo apt update
 sudo apt install libjemalloc2
 
 # Configure jemalloc
-export LD_PRELOAD=/usr/lib/"$(uname -i)"-linux-gnu/libjemalloc.so.2 $LD_PRELOAD
+export LD_PRELOAD=/usr/lib/"$(uname -i)"-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
 ```
 
 #### 2.2. Tcmalloc

docs/source/developer_guide/performance_and_debug/performance_benchmark.md

@@ -97,7 +97,8 @@ For local `dataset-path`, please set `hf-name` to its Hugging Face ID like
 First start serving your model:
 
 ```bash
-VLLM_USE_MODELSCOPE=True vllm serve Qwen/Qwen3-8B
+export VLLM_USE_MODELSCOPE=True 
+vllm serve Qwen/Qwen3-8B
 ```
 
 Then run the benchmarking script:
@@ -158,7 +159,7 @@ vllm bench throughput \
 If successful, you will see the following output
 
 ```shell
-Processed prompts: 100%|█| 10/10 [00:03<00:00,  2.74it/s, est. speed input: 351.02 toks/s, output: 351.02 t
+Processed prompts: 100%|█| 10/10 [00:03<00:00,  2.74it/s, est. speed input: 351.02 toks/s, output: 351.02 toks/s
 Throughput: 2.73 requests/s, 699.93 total tokens/s, 349.97 output tokens/s
 Total num prompt tokens:  1280
 Total num output tokens:  1280