Fix formatting in long code blocks (#10528)

docs/references/custom_chat_template.md

@@ -8,7 +8,10 @@ It should just work for most official models such as Llama-2/Llama-3.
 If needed, you can also override the chat template when launching the server:
 
 ```bash
-python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --chat-template llama-2
+python -m sglang.launch_server \
+  --model-path meta-llama/Llama-2-7b-chat-hf \
+  --port 30000 \
+  --chat-template llama-2
 ```
 
 If the chat template you are looking for is missing, you are welcome to contribute it or load it from a file.
@@ -30,7 +33,10 @@ You can load the JSON format, which is defined by `conversation.py`.
 ```
 
 ```bash
-python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --chat-template ./my_model_template.json
+python -m sglang.launch_server \
+  --model-path meta-llama/Llama-2-7b-chat-hf \
+  --port 30000 \
+  --chat-template ./my_model_template.json
 ```
 
 ## Jinja Format
@@ -38,5 +44,8 @@ python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port
 You can also use the [Jinja template format](https://huggingface.co/docs/transformers/main/en/chat_templating) as defined by Hugging Face Transformers.
 
 ```bash
-python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --chat-template ./my_model_template.jinja
+python -m sglang.launch_server \
+  --model-path meta-llama/Llama-2-7b-chat-hf \
+  --port 30000 \
+  --chat-template ./my_model_template.jinja
 ```

docs/references/multi_node_deployment/multi_node.md

@@ -7,9 +7,19 @@
 ```bash
 # replace 172.16.4.52:20000 with your own node ip address and port of the first node
 
-python3 -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-405B-Instruct --tp 16 --dist-init-addr 172.16.4.52:20000 --nnodes 2 --node-rank 0
+python3 -m sglang.launch_server \
+  --model-path meta-llama/Meta-Llama-3.1-405B-Instruct \
+  --tp 16 \
+  --dist-init-addr 172.16.4.52:20000 \
+  --nnodes 2 \
+  --node-rank 0
 
-python3 -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-405B-Instruct --tp 16 --dist-init-addr 172.16.4.52:20000 --nnodes 2 --node-rank 1
+python3 -m sglang.launch_server \
+  --model-path meta-llama/Meta-Llama-3.1-405B-Instruct \
+  --tp 16 \
+  --dist-init-addr 172.16.4.52:20000 \
+  --nnodes 2 \
+  --node-rank 1
 ```
 
 Note that LLama 405B (fp8) can also be launched on a single node.

docs/references/production_metrics.md

@@ -139,7 +139,10 @@ This section describes how to set up the monitoring stack (Prometheus + Grafana)
 1.  **Start your SGLang server with metrics enabled:**
 
     ```bash
-    python -m sglang.launch_server --model-path <your_model_path> --port 30000 --enable-metrics
+    python -m sglang.launch_server \
+      --model-path <your_model_path> \
+      --port 30000 \
+      --enable-metrics
     ```
     Replace `<your_model_path>` with the actual path to your model (e.g., `meta-llama/Meta-Llama-3.1-8B-Instruct`). Ensure the server is accessible from the monitoring stack (you might need `--host 0.0.0.0` if running in Docker). By default, the metrics endpoint will be available at `http://<sglang_server_host>:30000/metrics`.
 
@@ -212,6 +215,17 @@ You can customize the setup by modifying these files. For instance, you might ne
 
 #### Check if the metrics are being collected
 
-Run `python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 1024 --random-range-ratio 0.5` to generate some requests.
+Run:
+```
+python3 -m sglang.bench_serving \
+  --backend sglang \
+  --dataset-name random \
+  --num-prompts 3000 \
+  --random-input 1024 \
+  --random-output 1024 \
+  --random-range-ratio 0.5
+```
+
+to generate some requests.
 
 Then you should be able to see the metrics in the Grafana dashboard.