Make distinct `code` and `console` admonitions so readers are less likely to miss them (#20585)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

docs/design/plugin_system.md

@@ -13,7 +13,7 @@ Plugins are user-registered code that vLLM executes. Given vLLM's architecture (
 
 vLLM's plugin system uses the standard Python `entry_points` mechanism. This mechanism allows developers to register functions in their Python packages for use by other packages. An example of a plugin:
 
-??? Code
+??? code
 
     ```python
     # inside `setup.py` file

docs/design/v1/p2p_nccl_connector.md

@@ -61,7 +61,7 @@ To address the above issues, I have designed and developed a local Tensor memory
 
 # Install vLLM
 
-??? Commands
+??? console "Commands"
 
     ```shell
     # Enter the home directory or your working directory.
@@ -106,7 +106,7 @@ python3 disagg_prefill_proxy_xpyd.py &
 
 ### Prefill1 (e.g. 10.0.1.2 or 10.0.1.1)
 
-??? Command
+??? console "Command"
 
     ```shell
     VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=0 vllm serve {your model directory} \
@@ -128,7 +128,7 @@ python3 disagg_prefill_proxy_xpyd.py &
 
 ### Decode1 (e.g. 10.0.1.3 or 10.0.1.1)
 
-??? Command
+??? console "Command"
 
     ```shell
     VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=1 vllm serve {your model directory} \
@@ -150,7 +150,7 @@ python3 disagg_prefill_proxy_xpyd.py &
 
 ### Decode2 (e.g. 10.0.1.4 or 10.0.1.1)
 
-??? Command
+??? console "Command"
 
     ```shell
     VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=2 vllm serve {your model directory} \
@@ -172,7 +172,7 @@ python3 disagg_prefill_proxy_xpyd.py &
 
 ### Decode3 (e.g. 10.0.1.5 or 10.0.1.1)
 
-??? Command
+??? console "Command"
 
     ```shell
     VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=3 vllm serve {your model directory} \
@@ -203,7 +203,7 @@ python3 disagg_prefill_proxy_xpyd.py &
 
 ### Prefill1 (e.g. 10.0.1.2 or 10.0.1.1)
 
-??? Command
+??? console "Command"
 
     ```shell
     VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=0 vllm serve {your model directory} \
@@ -225,7 +225,7 @@ python3 disagg_prefill_proxy_xpyd.py &
 
 ### Prefill2 (e.g. 10.0.1.3 or 10.0.1.1)
 
-??? Command
+??? console "Command"
 
     ```shell
     VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=1 vllm serve {your model directory} \
@@ -247,7 +247,7 @@ python3 disagg_prefill_proxy_xpyd.py &
 
 ### Prefill3 (e.g. 10.0.1.4 or 10.0.1.1)
 
-??? Command
+??? console "Command"
 
     ```shell
     VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=2 vllm serve {your model directory} \
@@ -269,7 +269,7 @@ python3 disagg_prefill_proxy_xpyd.py &
 
 ### Decode1 (e.g. 10.0.1.5 or 10.0.1.1)
 
-??? Command
+??? console "Command"
 
     ```shell
     VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=3 vllm serve {your model directory} \
@@ -304,7 +304,7 @@ curl -X POST -s http://10.0.1.1:10001/v1/completions \
 
 # Benchmark
 
-??? Command
+??? console "Command"
 
     ```shell
     python3 benchmark_serving.py \

docs/design/v1/torch_compile.md

@@ -28,7 +28,7 @@ A unique aspect of vLLM's `torch.compile` integration, is that we guarantee all
 
 In the very verbose logs, we can see:
 
-??? Logs
+??? console "Logs"
 
       ```text
       DEBUG 03-07 03:06:52 [decorators.py:203] Start compiling function <code object forward at 0x7f08acf40c90, file "xxx/vllm/model_executor/models/llama.py", line 339>
@@ -110,7 +110,7 @@ Then it will also compile a specific kernel just for batch size `1, 2, 4, 8`. At
 
 When all the shapes are known, `torch.compile` can compare different configs, and often find some better configs to run the kernel. For example, we can see the following log:
 
-??? Logs
+??? console "Logs"
 
     ```
     AUTOTUNE mm(8x2048, 2048x3072)

docs/features/lora.md

@@ -29,7 +29,7 @@ We can now submit the prompts and call `llm.generate` with the `lora_request` pa
 of `LoRARequest` is a human identifiable name, the second parameter is a globally unique ID for the adapter and
 the third parameter is the path to the LoRA adapter.
 
-??? Code
+??? code
 
     ```python
     sampling_params = SamplingParams(
@@ -70,7 +70,7 @@ The server entrypoint accepts all other LoRA configuration parameters (`max_lora
 etc.), which will apply to all forthcoming requests. Upon querying the `/models` endpoint, we should see our LoRA along
 with its base model (if `jq` is not installed, you can follow [this guide](https://jqlang.org/download/) to install it.):
 
-??? Command
+??? console "Command"
 
     ```bash
     curl localhost:8000/v1/models | jq .
@@ -172,7 +172,7 @@ Alternatively, follow these example steps to implement your own plugin:
 
 1. Implement the LoRAResolver interface.
 
-    ??? Example of a simple S3 LoRAResolver implementation
+    ??? code "Example of a simple S3 LoRAResolver implementation"
 
         ```python
         import os
@@ -238,7 +238,7 @@ The new format of `--lora-modules` is mainly to support the display of parent mo
 - The `parent` field of LoRA model `sql-lora` now links to its base model `meta-llama/Llama-2-7b-hf`. This correctly reflects the hierarchical relationship between the base model and the LoRA adapter.
 - The `root` field points to the artifact location of the lora adapter.
 
-??? Command output
+??? console "Command output"
 
     ```bash
     $ curl http://localhost:8000/v1/models

docs/features/multimodal_inputs.md

@@ -20,7 +20,7 @@ To input multi-modal data, follow this schema in [vllm.inputs.PromptType][]:
 
 You can pass a single image to the `'image'` field of the multi-modal dictionary, as shown in the following examples:
 
-??? Code
+??? code
 
     ```python
     from vllm import LLM
@@ -68,7 +68,7 @@ Full example: <gh-file:examples/offline_inference/vision_language.py>
 
 To substitute multiple images inside the same text prompt, you can pass in a list of images instead:
 
-??? Code
+??? code
 
     ```python
     from vllm import LLM
@@ -146,7 +146,7 @@ for o in outputs:
 
 Multi-image input can be extended to perform video captioning. We show this with [Qwen2-VL](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) as it supports videos:
 
-??? Code
+??? code
 
     ```python
     from vllm import LLM
@@ -193,7 +193,7 @@ Full example: <gh-file:examples/offline_inference/audio_language.py>
 To input pre-computed embeddings belonging to a data type (i.e. image, video, or audio) directly to the language model,
 pass a tensor of shape `(num_items, feature_size, hidden_size of LM)` to the corresponding field of the multi-modal dictionary.
 
-??? Code
+??? code
 
     ```python
     from vllm import LLM
@@ -220,7 +220,7 @@ pass a tensor of shape `(num_items, feature_size, hidden_size of LM)` to the cor
 
 For Qwen2-VL and MiniCPM-V, we accept additional parameters alongside the embeddings:
 
-??? Code
+??? code
 
     ```python
     # Construct the prompt based on your model
@@ -288,7 +288,7 @@ vllm serve microsoft/Phi-3.5-vision-instruct --task generate \
 
 Then, you can use the OpenAI client as follows:
 
-??? Code
+??? code
 
     ```python
     from openai import OpenAI
@@ -366,7 +366,7 @@ vllm serve llava-hf/llava-onevision-qwen2-0.5b-ov-hf --task generate --max-model
 
 Then, you can use the OpenAI client as follows:
 
-??? Code
+??? code
 
     ```python
     from openai import OpenAI
@@ -430,7 +430,7 @@ vllm serve fixie-ai/ultravox-v0_5-llama-3_2-1b
 
 Then, you can use the OpenAI client as follows:
 
-??? Code
+??? code
 
     ```python
     import base64
@@ -486,7 +486,7 @@ Then, you can use the OpenAI client as follows:
 
 Alternatively, you can pass `audio_url`, which is the audio counterpart of `image_url` for image input:
 
-??? Code
+??? code
 
     ```python
     chat_completion_from_url = client.chat.completions.create(
@@ -531,7 +531,7 @@ pass a tensor of shape to the corresponding field of the multi-modal dictionary.
 For image embeddings, you can pass the base64-encoded tensor to the `image_embeds` field.
 The following example demonstrates how to pass image embeddings to the OpenAI server:
 
-??? Code
+??? code
 
     ```python
     image_embedding = torch.load(...)

docs/features/quantization/auto_awq.md

@@ -15,7 +15,7 @@ pip install autoawq
 
 After installing AutoAWQ, you are ready to quantize a model. Please refer to the [AutoAWQ documentation](https://casper-hansen.github.io/AutoAWQ/examples/#basic-quantization) for further details. Here is an example of how to quantize `mistralai/Mistral-7B-Instruct-v0.2`:
 
-??? Code
+??? code
 
     ```python
     from awq import AutoAWQForCausalLM
@@ -51,7 +51,7 @@ python examples/offline_inference/llm_engine_example.py \
 
 AWQ models are also supported directly through the LLM entrypoint:
 
-??? Code
+??? code
 
     ```python
     from vllm import LLM, SamplingParams

docs/features/quantization/bitblas.md

@@ -43,7 +43,7 @@ llm = LLM(
 
 ## Read gptq format checkpoint
 
-??? Code
+??? code
 
     ```python
     from vllm import LLM

docs/features/quantization/fp8.md

@@ -58,7 +58,7 @@ For FP8 quantization, we can recover accuracy with simple RTN quantization. We r
 
 Since simple RTN does not require data for weight quantization and the activations are quantized dynamically, we do not need any calibration data for this quantization flow.
 
-??? Code
+??? code
 
     ```python
     from llmcompressor.transformers import oneshot

docs/features/quantization/gguf.md

@@ -41,7 +41,7 @@ vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
 
 You can also use the GGUF model directly through the LLM entrypoint:
 
-??? Code
+??? code
 
       ```python
       from vllm import LLM, SamplingParams

docs/features/quantization/gptqmodel.md

@@ -31,7 +31,7 @@ After installing GPTQModel, you are ready to quantize a model. Please refer to t
 
 Here is an example of how to quantize `meta-llama/Llama-3.2-1B-Instruct`:
 
-??? Code
+??? code
 
     ```python
     from datasets import load_dataset
@@ -69,7 +69,7 @@ python examples/offline_inference/llm_engine_example.py \
 
 GPTQModel quantized models are also supported directly through the LLM entrypoint:
 
-??? Code
+??? code
 
     ```python
     from vllm import LLM, SamplingParams

docs/features/quantization/int4.md

@@ -53,7 +53,7 @@ When quantizing weights to INT4, you need sample data to estimate the weight upd
 It's best to use calibration data that closely matches your deployment data.
 For a general-purpose instruction-tuned model, you can use a dataset like `ultrachat`:
 
-??? Code
+??? code
 
     ```python
     from datasets import load_dataset
@@ -78,7 +78,7 @@ For a general-purpose instruction-tuned model, you can use a dataset like `ultra
 
 Now, apply the quantization algorithms:
 
-??? Code
+??? code
 
     ```python
     from llmcompressor.transformers import oneshot
@@ -141,7 +141,7 @@ lm_eval --model vllm \
 
 The following is an example of an expanded quantization recipe you can tune to your own use case:
 
-??? Code
+??? code
 
     ```python
     from compressed_tensors.quantization import (

docs/features/quantization/int8.md

@@ -54,7 +54,7 @@ When quantizing activations to INT8, you need sample data to estimate the activa
 It's best to use calibration data that closely matches your deployment data.
 For a general-purpose instruction-tuned model, you can use a dataset like `ultrachat`:
 
-??? Code
+??? code
 
     ```python
     from datasets import load_dataset
@@ -81,7 +81,7 @@ For a general-purpose instruction-tuned model, you can use a dataset like `ultra
 
 Now, apply the quantization algorithms:
 
-??? Code
+??? code
 
     ```python
     from llmcompressor.transformers import oneshot

docs/features/quantization/modelopt.md

@@ -14,7 +14,7 @@ You can quantize HuggingFace models using the example scripts provided in the Te
 
 Below is an example showing how to quantize a model using modelopt's PTQ API:
 
-??? Code
+??? code
 
     ```python
     import modelopt.torch.quantization as mtq
@@ -50,7 +50,7 @@ with torch.inference_mode():
 
 The quantized checkpoint can then be deployed with vLLM. As an example, the following code shows how to deploy `nvidia/Llama-3.1-8B-Instruct-FP8`, which is the FP8 quantized checkpoint derived from `meta-llama/Llama-3.1-8B-Instruct`, using vLLM:
 
-??? Code
+??? code
 
     ```python
     from vllm import LLM, SamplingParams

docs/features/quantization/quantized_kvcache.md

@@ -35,7 +35,7 @@ Studies have shown that FP8 E4M3 quantization typically only minimally degrades
 
 Here is an example of how to enable FP8 quantization:
 
-??? Code
+??? code
 
     ```python
     # To calculate kv cache scales on the fly enable the calculate_kv_scales
@@ -73,7 +73,7 @@ pip install llmcompressor
 
 Here's a complete example using `meta-llama/Llama-3.1-8B-Instruct` (most models can use this same pattern):
 
-??? Code
+??? code
 
     ```python
     from datasets import load_dataset

docs/features/quantization/quark.md

@@ -42,7 +42,7 @@ The Quark quantization process can be listed for 5 steps as below:
 Quark uses [Transformers](https://huggingface.co/docs/transformers/en/index)
 to fetch model and tokenizer.
 
-??? Code
+??? code
 
     ```python
     from transformers import AutoTokenizer, AutoModelForCausalLM
@@ -65,7 +65,7 @@ Quark uses the [PyTorch Dataloader](https://pytorch.org/tutorials/beginner/basic
 to load calibration data. For more details about how to use calibration datasets efficiently, please refer
 to [Adding Calibration Datasets](https://quark.docs.amd.com/latest/pytorch/calibration_datasets.html).
 
-??? Code
+??? code
 
     ```python
     from datasets import load_dataset
@@ -98,7 +98,7 @@ kv-cache and the quantization algorithm is AutoSmoothQuant.
     AutoSmoothQuant config file for Llama is
     `examples/torch/language_modeling/llm_ptq/models/llama/autosmoothquant_config.json`.
 
-??? Code
+??? code
 
     ```python
     from quark.torch.quantization import (Config, QuantizationConfig,
@@ -145,7 +145,7 @@ HuggingFace `safetensors`, you can refer to
 [HuggingFace format exporting](https://quark.docs.amd.com/latest/pytorch/export/quark_export_hf.html)
 for more exporting format details.
 
-??? Code
+??? code
 
     ```python
     import torch
@@ -176,7 +176,7 @@ for more exporting format details.
 
 Now, you can load and run the Quark quantized model directly through the LLM entrypoint:
 
-??? Code
+??? code
 
     ```python
     from vllm import LLM, SamplingParams

docs/features/quantization/torchao.md

@@ -15,7 +15,7 @@ pip install \
 ## Quantizing HuggingFace Models
 You can quantize your own huggingface model with torchao, e.g. [transformers](https://huggingface.co/docs/transformers/main/en/quantization/torchao) and [diffusers](https://huggingface.co/docs/diffusers/en/quantization/torchao), and save the checkpoint to huggingface hub like [this](https://huggingface.co/jerryzh168/llama3-8b-int8wo) with the following example code:
 
-??? Code
+??? code
 
     ```Python
     import torch

docs/features/reasoning_outputs.md

@@ -33,7 +33,7 @@ vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B \
 
 Next, make a request to the model that should return the reasoning content in the response.
 
-??? Code
+??? code
 
     ```python
     from openai import OpenAI
@@ -70,7 +70,7 @@ The `reasoning_content` field contains the reasoning steps that led to the final
 
 Streaming chat completions are also supported for reasoning models. The `reasoning_content` field is available in the `delta` field in [chat completion response chunks](https://platform.openai.com/docs/api-reference/chat/streaming).
 
-??? Json
+??? console "Json"
 
     ```json
     {
@@ -95,7 +95,7 @@ Streaming chat completions are also supported for reasoning models. The `reasoni
 
 OpenAI Python client library does not officially support `reasoning_content` attribute for streaming output. But the client supports extra attributes in the response. You can use `hasattr` to check if the `reasoning_content` attribute is present in the response. For example:
 
-??? Code
+??? code
 
     ```python
     from openai import OpenAI
@@ -152,7 +152,7 @@ Remember to check whether the `reasoning_content` exists in the response before
 
 The reasoning content is also available when both tool calling and the reasoning parser are enabled. Additionally, tool calling only parses functions from the `content` field, not from the `reasoning_content`.
 
-??? Code
+??? code
 
     ```python
     from openai import OpenAI
@@ -200,7 +200,7 @@ For more examples, please refer to <gh-file:examples/online_serving/openai_chat_
 
 You can add a new `ReasoningParser` similar to <gh-file:vllm/reasoning/deepseek_r1_reasoning_parser.py>.
 
-??? Code
+??? code
 
     ```python
     # import the required packages
@@ -258,7 +258,7 @@ You can add a new `ReasoningParser` similar to <gh-file:vllm/reasoning/deepseek_
 
 Additionally, to enable structured output, you'll need to create a new `Reasoner` similar to the one in <gh-file:vllm/reasoning/deepseek_r1_reasoning_parser.py>.
 
-??? Code
+??? code
 
     ```python
     @dataclass

docs/features/spec_decode.md

@@ -18,7 +18,7 @@ Speculative decoding is a technique which improves inter-token latency in memory
 
 The following code configures vLLM in an offline mode to use speculative decoding with a draft model, speculating 5 tokens at a time.
 
-??? Code
+??? code
 
     ```python
     from vllm import LLM, SamplingParams
@@ -62,7 +62,7 @@ python -m vllm.entrypoints.openai.api_server \
 
 Then use a client:
 
-??? Code
+??? code
 
     ```python
     from openai import OpenAI
@@ -103,7 +103,7 @@ Then use a client:
 The following code configures vLLM to use speculative decoding where proposals are generated by
 matching n-grams in the prompt. For more information read [this thread.](https://x.com/joao_gante/status/1747322413006643259)
 
-??? Code
+??? code
 
     ```python
     from vllm import LLM, SamplingParams
@@ -137,7 +137,7 @@ draft models that conditioning draft predictions on both context vectors and sam
 For more information see [this blog](https://pytorch.org/blog/hitchhikers-guide-speculative-decoding/) or
 [this technical report](https://arxiv.org/abs/2404.19124).
 
-??? Code
+??? code
 
     ```python
     from vllm import LLM, SamplingParams
@@ -185,7 +185,7 @@ A variety of speculative models of this type are available on HF hub:
 The following code configures vLLM to use speculative decoding where proposals are generated by
 an [EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency)](https://arxiv.org/pdf/2401.15077) based draft model. A more detailed example for offline mode, including how to extract request level acceptance rate, can be found [here](gh-file:examples/offline_inference/eagle.py).
 
-??? Code
+??? code
 
     ```python
     from vllm import LLM, SamplingParams

docs/features/structured_outputs.md

@@ -33,7 +33,7 @@ text.
 
 Now let´s see an example for each of the cases, starting with the `guided_choice`, as it´s the easiest one:
 
-??? Code
+??? code
 
     ```python
     from openai import OpenAI
@@ -55,7 +55,7 @@ Now let´s see an example for each of the cases, starting with the `guided_choic
 
 The next example shows how to use the `guided_regex`. The idea is to generate an email address, given a simple regex template:
 
-??? Code
+??? code
 
     ```python
     completion = client.chat.completions.create(
@@ -79,7 +79,7 @@ For this we can use the `guided_json` parameter in two different ways:
 
 The next example shows how to use the `guided_json` parameter with a Pydantic model:
 
-??? Code
+??? code
 
     ```python
     from pydantic import BaseModel
@@ -127,7 +127,7 @@ difficult to use, but it´s really powerful. It allows us to define complete
 languages like SQL queries. It works by using a context free EBNF grammar.
 As an example, we can use to define a specific format of simplified SQL queries:
 
-??? Code
+??? code
 
     ```python
     simplified_sql_grammar = """
@@ -169,7 +169,7 @@ vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-7B --reasoning-parser deepseek_r
 
 Note that you can use reasoning with any provided structured outputs feature. The following uses one with JSON schema:
 
-??? Code
+??? code
 
     ```python
     from pydantic import BaseModel
@@ -212,7 +212,7 @@ For the following examples, vLLM was setup using `vllm serve meta-llama/Llama-3.
 
 Here is a simple example demonstrating how to get structured output using Pydantic models:
 
-??? Code
+??? code
 
     ```python
     from pydantic import BaseModel
@@ -248,7 +248,7 @@ Age: 28
 
 Here is a more complex example using nested Pydantic models to handle a step-by-step math solution:
 
-??? Code
+??? code
 
     ```python
     from typing import List
@@ -308,7 +308,7 @@ These parameters can be used in the same way as the parameters from the Online
 Serving examples above. One example for the usage of the `choice` parameter is
 shown below:
 
-??? Code
+??? code
 
     ```python
     from vllm import LLM, SamplingParams

docs/features/tool_calling.md

@@ -15,7 +15,7 @@ vllm serve meta-llama/Llama-3.1-8B-Instruct \
 
 Next, make a request to the model that should result in it using the available tools:
 
-??? Code
+??? code
 
     ```python
     from openai import OpenAI
@@ -320,7 +320,7 @@ A tool parser plugin is a Python file containing one or more ToolParser implemen
 
 Here is a summary of a plugin file:
 
-??? Code
+??? code
 
     ```python