[Doc]: fix typos in .md files (including those of #23751) (#23825)

Signed-off-by: Didier Durand <durand.didier@gmail.com>

docs/contributing/ci/update_pytorch_version.md

@@ -90,7 +90,7 @@ address the long build time at its source, the current workaround is to set `VLL
 to a custom branch provided by @khluu (`VLLM_CI_BRANCH=khluu/use_postmerge_q`)
 when manually triggering a build on Buildkite. This branch accomplishes two things:
 
-1. Increase the timeout limit to 10 hours so that the build doesn't timeout.
+1. Increase the timeout limit to 10 hours so that the build doesn't time out.
 2. Allow the compiled artifacts to be written to the vLLM sccache S3 bucket
 to warm it up so that future builds are faster.
 

docs/contributing/model/multimodal.md

@@ -855,7 +855,7 @@ Examples:
 
 ### Custom HF processor
 
-Some models don't define a HF processor class on HF Hub. In that case, you can define a custom HF processor that has the same call signature as HF processors and pass it to [_call_hf_processor][vllm.multimodal.processing.BaseMultiModalProcessor._call_hf_processor].
+Some models don't define an HF processor class on HF Hub. In that case, you can define a custom HF processor that has the same call signature as HF processors and pass it to [_call_hf_processor][vllm.multimodal.processing.BaseMultiModalProcessor._call_hf_processor].
 
 Examples:
 

docs/deployment/frameworks/lobe-chat.md

@@ -6,6 +6,6 @@ Supports speech-synthesis, multi-modal, and extensible (function call) plugin sy
 
 One-click FREE deployment of your private OpenAI ChatGPT/Claude/Gemini/Groq/Ollama chat application.
 
-It supports vLLM as a AI model provider to efficiently serve large language models.
+It supports vLLM as an AI model provider to efficiently serve large language models.
 
 For details, see the tutorial [Using vLLM in LobeChat](https://lobehub.com/docs/usage/providers/vllm).

docs/deployment/k8s.md

@@ -380,7 +380,7 @@ INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
 
 ### Startup Probe or Readiness Probe Failure, container log contains "KeyboardInterrupt: terminated"
 
-If the startup or readiness probe failureThreshold is too low for the time needed to startup the server, Kubernetes scheduler will kill the container. A couple of indications that this has happened:
+If the startup or readiness probe failureThreshold is too low for the time needed to start up the server, Kubernetes scheduler will kill the container. A couple of indications that this has happened:
 
 1. container log contains "KeyboardInterrupt: terminated"
 2. `kubectl get events` shows message `Container $NAME failed startup probe, will be restarted`

docs/design/fused_moe_modular_kernel.md

@@ -138,7 +138,7 @@ Typically a FusedMoEPrepareAndFinalize type is backed by an All2All Dispatch & C
 
 #### Step 1: Add an All2All manager
 
-The purpose of the All2All Manager is to setup the All2All kernel implementations. The `FusedMoEPrepareAndFinalize` implementations typically fetch a kernel-implementation "handle" from the All2All Manager to invoke the Dispatch and Combine functions. Please look at the All2All Manager implementations [here](gh-file:vllm/distributed/device_communicators/all2all.py).
+The purpose of the All2All Manager is to set up the All2All kernel implementations. The `FusedMoEPrepareAndFinalize` implementations typically fetch a kernel-implementation "handle" from the All2All Manager to invoke the Dispatch and Combine functions. Please look at the All2All Manager implementations [here](gh-file:vllm/distributed/device_communicators/all2all.py).
 
 #### Step 2: Add a FusedMoEPrepareAndFinalize Type
 

docs/design/metrics.md

@@ -99,11 +99,11 @@ http_request_duration_seconds_count{handler="/v1/completions",method="POST"} 201
 
 ### Multi-process Mode
 
-In v0, metrics are collected in the engine core process and we use multi-process mode to make them available in the API server process. See <gh-pr:7279>.
+In v0, metrics are collected in the engine core process and we use multiprocess mode to make them available in the API server process. See <gh-pr:7279>.
 
 ### Built in Python/Process Metrics
 
-The following metrics are supported by default by `prometheus_client`, but they are not exposed when multi-process mode is used:
+The following metrics are supported by default by `prometheus_client`, but they are not exposed when multiprocess mode is used:
 
 - `python_gc_objects_collected_total`
 - `python_gc_objects_uncollectable_total`

docs/features/lora.md

@@ -52,7 +52,7 @@ Check out <gh-file:examples/offline_inference/multilora_inference.py> for an exa
 ## Serving LoRA Adapters
 
 LoRA adapted models can also be served with the Open-AI compatible vLLM server. To do so, we use
-`--lora-modules {name}={path} {name}={path}` to specify each LoRA module when we kickoff the server:
+`--lora-modules {name}={path} {name}={path}` to specify each LoRA module when we kick off the server:
 
 ```bash
 vllm serve meta-llama/Llama-2-7b-hf \

docs/features/reasoning_outputs.md

@@ -143,7 +143,7 @@ OpenAI Python client library does not officially support `reasoning_content` att
             print(content, end="", flush=True)
     ```
 
-Remember to check whether the `reasoning_content` exists in the response before accessing it. You could checkout the [example](https://github.com/vllm-project/vllm/blob/main/examples/online_serving/openai_chat_completion_with_reasoning_streaming.py).
+Remember to check whether the `reasoning_content` exists in the response before accessing it. You could check out the [example](https://github.com/vllm-project/vllm/blob/main/examples/online_serving/openai_chat_completion_with_reasoning_streaming.py).
 
 ## Tool Calling
 

docs/features/structured_outputs.md

@@ -205,7 +205,7 @@ This section covers the OpenAI beta wrapper over the `client.chat.completions.cr
 
 At the time of writing (`openai==1.54.4`), this is a "beta" feature in the OpenAI client library. Code reference can be found [here](https://github.com/openai/openai-python/blob/52357cff50bee57ef442e94d78a0de38b4173fc2/src/openai/resources/beta/chat/completions.py#L100-L104).
 
-For the following examples, vLLM was setup using `vllm serve meta-llama/Llama-3.1-8B-Instruct`
+For the following examples, vLLM was set up using `vllm serve meta-llama/Llama-3.1-8B-Instruct`
 
 Here is a simple example demonstrating how to get structured output using Pydantic models:
 

docs/getting_started/installation/aws_neuron.md

@@ -140,8 +140,8 @@ Alternatively, users can directly call the NxDI library to trace and compile you
 
 - `NEURON_COMPILED_ARTIFACTS`: set this environment variable to point to your pre-compiled model artifacts directory to avoid
   compilation time upon server initialization. If this variable is not set, the Neuron module will perform compilation and save the
-  artifacts under `neuron-compiled-artifacts/{unique_hash}/` sub-directory in the model path. If this environment variable is set,
-  but the directory does not exist, or the contents are invalid, Neuron will also fallback to a new compilation and store the artifacts
+  artifacts under `neuron-compiled-artifacts/{unique_hash}/` subdirectory in the model path. If this environment variable is set,
+  but the directory does not exist, or the contents are invalid, Neuron will also fall back to a new compilation and store the artifacts
   under this specified path.
 - `NEURON_CONTEXT_LENGTH_BUCKETS`: Bucket sizes for context encoding. (Only applicable to `transformers-neuronx` backend).
 - `NEURON_TOKEN_GEN_BUCKETS`: Bucket sizes for token generation. (Only applicable to `transformers-neuronx` backend).

docs/getting_started/installation/cpu/apple.inc.md

@@ -1,6 +1,6 @@
 # --8<-- [start:installation]
 
-vLLM has experimental support for macOS with Apple silicon. For now, users must build from source to natively run on macOS.
+vLLM has experimental support for macOS with Apple Silicon. For now, users must build from source to natively run on macOS.
 
 Currently the CPU implementation for macOS supports FP32 and FP16 datatypes.
 

docs/getting_started/installation/gpu/cuda.inc.md

@@ -48,7 +48,7 @@ uv pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VE
 
 #### Install the latest code
 
-LLM inference is a fast-evolving field, and the latest code may contain bug fixes, performance improvements, and new features that are not released yet. To allow users to try the latest code without waiting for the next release, vLLM provides wheels for Linux running on a x86 platform with CUDA 12 for every commit since `v0.5.3`.
+LLM inference is a fast-evolving field, and the latest code may contain bug fixes, performance improvements, and new features that are not released yet. To allow users to try the latest code without waiting for the next release, vLLM provides wheels for Linux running on an x86 platform with CUDA 12 for every commit since `v0.5.3`.
 
 ```bash
 uv pip install -U vllm \

docs/getting_started/installation/gpu/rocm.inc.md

@@ -149,7 +149,7 @@ Build a docker image from <gh-file:docker/Dockerfile.rocm_base> which setup ROCm
 **This step is optional as this rocm_base image is usually prebuilt and store at [Docker Hub](https://hub.docker.com/r/rocm/vllm-dev) under tag `rocm/vllm-dev:base` to speed up user experience.**
 If you choose to build this rocm_base image yourself, the steps are as follows.
 
-It is important that the user kicks off the docker build using buildkit. Either the user put DOCKER_BUILDKIT=1 as environment variable when calling docker build command, or the user needs to setup buildkit in the docker daemon configuration /etc/docker/daemon.json as follows and restart the daemon:
+It is important that the user kicks off the docker build using buildkit. Either the user put DOCKER_BUILDKIT=1 as environment variable when calling docker build command, or the user needs to set up buildkit in the docker daemon configuration /etc/docker/daemon.json as follows and restart the daemon:
 
 ```json
 {
@@ -170,7 +170,7 @@ DOCKER_BUILDKIT=1 docker build \
 #### Build an image with vLLM
 
 First, build a docker image from <gh-file:docker/Dockerfile.rocm> and launch a docker container from the image.
-It is important that the user kicks off the docker build using buildkit. Either the user put `DOCKER_BUILDKIT=1` as environment variable when calling docker build command, or the user needs to setup buildkit in the docker daemon configuration /etc/docker/daemon.json as follows and restart the daemon:
+It is important that the user kicks off the docker build using buildkit. Either the user put `DOCKER_BUILDKIT=1` as environment variable when calling docker build command, or the user needs to set up buildkit in the docker daemon configuration /etc/docker/daemon.json as follows and restart the daemon:
 
 ```bash
 {

docs/models/pooling_models.md

@@ -258,4 +258,4 @@ Expected output:
 {"id":"embd-5c21fc9a5c9d4384a1b021daccaf9f64","object":"list","created":1745476417,"model":"jinaai/jina-embeddings-v3","data":[{"index":0,"object":"embedding","embedding":[-0.3828125,-0.1357421875,0.03759765625,0.125,0.21875,0.09521484375,-0.003662109375,0.1591796875,-0.130859375,-0.0869140625,-0.1982421875,0.1689453125,-0.220703125,0.1728515625,-0.2275390625,-0.0712890625,-0.162109375,-0.283203125,-0.055419921875,-0.0693359375,0.031982421875,-0.04052734375,-0.2734375,0.1826171875,-0.091796875,0.220703125,0.37890625,-0.0888671875,-0.12890625,-0.021484375,-0.0091552734375,0.23046875]}],"usage":{"prompt_tokens":8,"total_tokens":8,"completion_tokens":0,"prompt_tokens_details":null}}
 ```
 
-A openai client example can be found here: <gh-file:examples/online_serving/openai_embedding_matryoshka_fy.py>
+An OpenAI client example can be found here: <gh-file:examples/online_serving/openai_embedding_matryoshka_fy.py>

docs/models/supported_models.md

@@ -40,7 +40,7 @@ If it is `TransformersForCausalLM` or `TransformersForMultimodalLM` then it mean
 
 #### Custom models
 
-If a model is neither supported natively by vLLM or Transformers, it can still be used in vLLM!
+If a model is neither supported natively by vLLM nor Transformers, it can still be used in vLLM!
 
 For a model to be compatible with the Transformers backend for vLLM it must:
 

docs/usage/usage_stats.md

@@ -51,7 +51,7 @@ tail ~/.config/vllm/usage_stats.json
 
 ## Opting out
 
-You can opt-out of usage stats collection by setting the `VLLM_NO_USAGE_STATS` or `DO_NOT_TRACK` environment variable, or by creating a `~/.config/vllm/do_not_track` file:
+You can opt out of usage stats collection by setting the `VLLM_NO_USAGE_STATS` or `DO_NOT_TRACK` environment variable, or by creating a `~/.config/vllm/do_not_track` file:
 
 ```bash
 # Any of the following methods can disable usage stats collection