[doc] Fold long code blocks to improve readability

docs/ci/update_pytorch_version.md

@@ -91,7 +91,7 @@ source to unblock the update process.
 ### FlashInfer
 Here is how to build and install it from source with torch2.7.0+cu128 in vLLM [Dockerfile](https://github.com/vllm-project/vllm/blob/27bebcd89792d5c4b08af7a65095759526f2f9e1/docker/Dockerfile#L259-L271):
 
-```
+```bash
 export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.9 9.0 10.0+PTX'
 export FLASHINFER_ENABLE_SM90=1
 uv pip install --system --no-build-isolation "git+https://github.com/flashinfer-ai/[email protected]"
@@ -105,14 +105,14 @@ team if you want to get the package published there.
 ### xFormers
 Similar to FlashInfer, here is how to build and install xFormers from source:
 
-```
+```bash
 export TORCH_CUDA_ARCH_LIST='7.0 7.5 8.0 8.9 9.0 10.0+PTX'
 MAX_JOBS=16 uv pip install --system --no-build-isolation "git+https://github.com/facebookresearch/[email protected]"
 ```
 
 ### Mamba
 
-```
+```bash
 uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/[email protected]"
 ```
 

docs/cli/README.md

@@ -16,35 +16,33 @@ vllm {chat,complete,serve,bench,collect-env,run-batch}
 
 Start the vLLM OpenAI Compatible API server.
 
-Examples:
+??? Examples
 
-```bash
-# Start with a model
-vllm serve meta-llama/Llama-2-7b-hf
+    ```bash
+    # Start with a model
+    vllm serve meta-llama/Llama-2-7b-hf
 
-# Specify the port
-vllm serve meta-llama/Llama-2-7b-hf --port 8100
+    # Specify the port
+    vllm serve meta-llama/Llama-2-7b-hf --port 8100
 
-# Check with --help for more options
-# To list all groups
-vllm serve --help=listgroup
+    # Check with --help for more options
+    # To list all groups
+    vllm serve --help=listgroup
 
-# To view a argument group
-vllm serve --help=ModelConfig
+    # To view a argument group
+    vllm serve --help=ModelConfig
 
-# To view a single argument
-vllm serve --help=max-num-seqs
+    # To view a single argument
+    vllm serve --help=max-num-seqs
 
-# To search by keyword
-vllm serve --help=max
-```
+    # To search by keyword
+    vllm serve --help=max
+    ```
 
 ## chat
 
 Generate chat completions via the running API server.
 
-Examples:
-
 ```bash
 # Directly connect to localhost API without arguments
 vllm chat
@@ -60,8 +58,6 @@ vllm chat --quick "hi"
 
 Generate text completions based on the given prompt via the running API server.
 
-Examples:
-
 ```bash
 # Directly connect to localhost API without arguments
 vllm complete
@@ -73,6 +69,8 @@ vllm complete --url http://{vllm-serve-host}:{vllm-serve-port}/v1
 vllm complete --quick "The future of AI is"
 ```
 
+</details>
+
 ## bench
 
 Run benchmark tests for latency online serving throughput and offline inference throughput.
@@ -89,8 +87,6 @@ vllm bench {latency, serve, throughput}
 
 Benchmark the latency of a single batch of requests.
 
-Example:
-
 ```bash
 vllm bench latency \
     --model meta-llama/Llama-3.2-1B-Instruct \
@@ -104,8 +100,6 @@ vllm bench latency \
 
 Benchmark the online serving throughput.
 
-Example:
-
 ```bash
 vllm bench serve \
     --model meta-llama/Llama-3.2-1B-Instruct \
@@ -120,8 +114,6 @@ vllm bench serve \
 
 Benchmark offline inference throughput.
 
-Example:
-
 ```bash
 vllm bench throughput \
     --model meta-llama/Llama-3.2-1B-Instruct \
@@ -143,7 +135,8 @@ vllm collect-env
 
 Run batch prompts and write results to file.
 
-Examples:
+<details>
+<summary>Examples</summary>
 
 ```bash
 # Running with a local file
@@ -159,6 +152,8 @@ vllm run-batch \
     --model meta-llama/Meta-Llama-3-8B-Instruct
 ```
 
+</details>
+
 ## More Help
 
 For detailed options of any subcommand, use:

docs/configuration/conserving_memory.md

@@ -57,19 +57,21 @@ By default, we optimize model inference using CUDA graphs which take up extra me
 
 You can adjust `compilation_config` to achieve a better balance between inference speed and memory usage:
 
-```python
-from vllm import LLM
-from vllm.config import CompilationConfig, CompilationLevel
-
-llm = LLM(
-    model="meta-llama/Llama-3.1-8B-Instruct",
-    compilation_config=CompilationConfig(
-        level=CompilationLevel.PIECEWISE,
-        # By default, it goes up to max_num_seqs
-        cudagraph_capture_sizes=[1, 2, 4, 8, 16],
-    ),
-)
-```
+??? Code
+
+    ```python
+    from vllm import LLM
+    from vllm.config import CompilationConfig, CompilationLevel
+
+    llm = LLM(
+        model="meta-llama/Llama-3.1-8B-Instruct",
+        compilation_config=CompilationConfig(
+            level=CompilationLevel.PIECEWISE,
+            # By default, it goes up to max_num_seqs
+            cudagraph_capture_sizes=[1, 2, 4, 8, 16],
+        ),
+    )
+    ```
 
 You can disable graph capturing completely via the `enforce_eager` flag:
 
@@ -127,18 +129,20 @@ reduce the size of the processed multi-modal inputs, which in turn saves memory.
 
 Here are some examples:
 
-```python
-from vllm import LLM
+??? Code
 
-# Available for Qwen2-VL series models
-llm = LLM(model="Qwen/Qwen2.5-VL-3B-Instruct",
-          mm_processor_kwargs={
-              "max_pixels": 768 * 768,  # Default is 1280 * 28 * 28
-          })
-
-# Available for InternVL series models
-llm = LLM(model="OpenGVLab/InternVL2-2B",
-          mm_processor_kwargs={
-              "max_dynamic_patch": 4,  # Default is 12
-          })
-```
+    ```python
+    from vllm import LLM
+
+    # Available for Qwen2-VL series models
+    llm = LLM(model="Qwen/Qwen2.5-VL-3B-Instruct",
+            mm_processor_kwargs={
+                "max_pixels": 768 * 768,  # Default is 1280 * 28 * 28
+            })
+
+    # Available for InternVL series models
+    llm = LLM(model="OpenGVLab/InternVL2-2B",
+            mm_processor_kwargs={
+                "max_dynamic_patch": 4,  # Default is 12
+            })
+    ```

docs/configuration/env_vars.md

@@ -7,6 +7,8 @@ vLLM uses the following environment variables to configure the system:
 
     All environment variables used by vLLM are prefixed with `VLLM_`. **Special care should be taken for Kubernetes users**: please do not name the service as `vllm`, otherwise environment variables set by Kubernetes might conflict with vLLM's environment variables, because [Kubernetes sets environment variables for each service with the capitalized service name as the prefix](https://kubernetes.io/docs/concepts/services-networking/service/#environment-variables).
 
-```python
---8<-- "vllm/envs.py:env-vars-definition"
-```
+??? Code
+
+    ```python
+    --8<-- "vllm/envs.py:env-vars-definition"
+    ```

docs/contributing/README.md

@@ -93,25 +93,27 @@ For additional features and advanced configurations, refer to the official [MkDo
 
 ## Testing
 
-```bash
-pip install -r requirements/dev.txt
+??? note "Commands"
 
-# Linting, formatting and static type checking
-pre-commit install --hook-type pre-commit --hook-type commit-msg
+    ```bash
+    pip install -r requirements/dev.txt
 
-# You can manually run pre-commit with
-pre-commit run --all-files
+    # Linting, formatting and static type checking
+    pre-commit install --hook-type pre-commit --hook-type commit-msg
 
-# To manually run something from CI that does not run
-# locally by default, you can run:
-pre-commit run mypy-3.9 --hook-stage manual --all-files
+    # You can manually run pre-commit with
+    pre-commit run --all-files
 
-# Unit tests
-pytest tests/
+    # To manually run something from CI that does not run
+    # locally by default, you can run:
+    pre-commit run mypy-3.9 --hook-stage manual --all-files
 
-# Run tests for a single test file with detailed output
-pytest -s -v tests/test_logger.py
-```
+    # Unit tests
+    pytest tests/
+
+    # Run tests for a single test file with detailed output
+    pytest -s -v tests/test_logger.py
+    ```
 
 !!! tip
     Since the <gh-file:docker/Dockerfile> ships with Python 3.12, all tests in CI (except `mypy`) are run with Python 3.12.

docs/contributing/model/basic.md

@@ -27,33 +27,35 @@ All vLLM modules within the model must include a `prefix` argument in their cons
 
 The initialization code should look like this:
 
-```python
-from torch import nn
-from vllm.config import VllmConfig
-from vllm.attention import Attention
-
-class MyAttention(nn.Module):
-    def __init__(self, vllm_config: VllmConfig, prefix: str):
-        super().__init__()
-        self.attn = Attention(prefix=f"{prefix}.attn")
-
-class MyDecoderLayer(nn.Module):
-    def __init__(self, vllm_config: VllmConfig, prefix: str):
-        super().__init__()
-        self.self_attn = MyAttention(prefix=f"{prefix}.self_attn")
-
-class MyModel(nn.Module):
-    def __init__(self, vllm_config: VllmConfig, prefix: str):
-        super().__init__()
-        self.layers = nn.ModuleList(
-            [MyDecoderLayer(vllm_config, prefix=f"{prefix}.layers.{i}") for i in range(vllm_config.model_config.hf_config.num_hidden_layers)]
-        )
-
-class MyModelForCausalLM(nn.Module):
-    def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__()
-        self.model = MyModel(vllm_config, prefix=f"{prefix}.model")
-```
+??? Code
+
+    ```python
+    from torch import nn
+    from vllm.config import VllmConfig
+    from vllm.attention import Attention
+
+    class MyAttention(nn.Module):
+        def __init__(self, vllm_config: VllmConfig, prefix: str):
+            super().__init__()
+            self.attn = Attention(prefix=f"{prefix}.attn")
+
+    class MyDecoderLayer(nn.Module):
+        def __init__(self, vllm_config: VllmConfig, prefix: str):
+            super().__init__()
+            self.self_attn = MyAttention(prefix=f"{prefix}.self_attn")
+
+    class MyModel(nn.Module):
+        def __init__(self, vllm_config: VllmConfig, prefix: str):
+            super().__init__()
+            self.layers = nn.ModuleList(
+                [MyDecoderLayer(vllm_config, prefix=f"{prefix}.layers.{i}") for i in range(vllm_config.model_config.hf_config.num_hidden_layers)]
+            )
+
+    class MyModelForCausalLM(nn.Module):
+        def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
+            super().__init__()
+            self.model = MyModel(vllm_config, prefix=f"{prefix}.model")
+    ```
 
 ### Computation Code