[main][Docs] Fix spelling errors across documentation (#6649)

Fix various spelling mistakes in the project documentation to improve clarity and correctness. - vLLM version: v0.15.0 - vLLM main: d7e17aaacd --------- Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
2026-02-10 11:14:57 +08:00
parent 5b8e47cb68
commit 1c7d1163f5
30 changed files with 67 additions and 67 deletions
--- a/docs/source/tutorials/DeepSeek-R1.md
+++ b/docs/source/tutorials/DeepSeek-R1.md
@@ -3,7 +3,7 @@
 ## Introduction

 DeepSeek-R1 is a high-performance Mixture-of-Experts (MoE) large language model developed by DeepSeek Company. It excels in complex logical reasoning, mathematical problem-solving, and code generation. By dynamically activating its expert networks, it delivers exceptional performance while maintaining computational efficiency. Building upon R1, DeepSeek-R1-W8A8 is a fully quantized version of the model. It employs 8-bit integer (INT8) quantization for both weights and activations, which significantly reduces the model's memory footprint and computational requirements, enabling more efficient deployment and application in resource-constrained environments.
-This article takes the deepseek- R1-W8A8 version as an example to introduce the deployment of the R1 series models.
+This article takes the `DeepSeek-R1-W8A8` version as an example to introduce the deployment of the R1 series models.

 ## Supported Features

@@ -25,7 +25,7 @@ If you want to deploy multi-node environment, you need to verify multi-node comm

 ### Installation

-You can using our official docker image to run `DeepSeek-R1-W8A8` directly.
+You can use our official docker image to run `DeepSeek-R1-W8A8` directly.

 Select an image based on your machine type and start the docker image on your node, refer to [using docker](../installation.md#set-up-using-docker).

@@ -293,7 +293,7 @@ Run performance evaluation of `DeepSeek-R1-W8A8` as an example.

 Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.

-There are three `vllm bench` subcommand:
+There are three `vllm bench` subcommands:

 - `latency`: Benchmark the latency of a single batch of requests.
 - `serve`: Benchmark the online serving throughput.
--- a/docs/source/tutorials/DeepSeek-V3.1.md
+++ b/docs/source/tutorials/DeepSeek-V3.1.md
@@ -38,7 +38,7 @@ If you want to deploy multi-node environment, you need to verify multi-node comm

 ### Installation

-You can using our official docker image to run `DeepSeek-V3.1` directly.
+You can use our official docker image to run `DeepSeek-V3.1` directly.

 Select an image based on your machine type and start the docker image on your node, refer to [using docker](../installation.md#set-up-using-docker).

@@ -96,7 +96,7 @@ nic_name="xxxx"
 local_ip="xxxx"

 # [Optional] jemalloc
-# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
+# jemalloc is for better performance, if `libjemalloc.so` is installed on your machine, you can turn it on.
 # export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD

 # AIV
@@ -158,7 +158,7 @@ local_ip="xxxx"
 node0_ip="xxxx"

 # [Optional] jemalloc
-# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
+# jemalloc is for better performance, if `libjemalloc.so` is installed on your machine, you can turn it on.
 # export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD

 export HCCL_IF_IP=$local_ip
@@ -210,7 +210,7 @@ local_ip="xxx"
 node0_ip="xxxx"

 # [Optional] jemalloc
-# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
+# jemalloc is for better performance, if `libjemalloc.so` is installed on your machine, you can turn it on.
 # export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD

 export HCCL_IF_IP=$local_ip
@@ -275,7 +275,7 @@ local_ip="141.xx.xx.1"
 node0_ip="xxxx"

 # [Optional] jemalloc
-# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
+# jemalloc is for better performance, if `libjemalloc.so` is installed on your machine, you can turn it on.
 # export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD

 export HCCL_IF_IP=$local_ip
@@ -352,7 +352,7 @@ local_ip="141.xx.xx.2"
 node0_ip="xxxx"

 # [Optional] jemalloc
-# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
+# jemalloc is for better performance, if `libjemalloc.so` is installed on your machine, you can turn it on.
 # export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD

 export HCCL_IF_IP=$local_ip
@@ -429,7 +429,7 @@ local_ip="141.xx.xx.3"
 node0_ip="xxxx"

 # [Optional] jemalloc
-# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
+# jemalloc is for better performance, if `libjemalloc.so` is installed on your machine, you can turn it on.
 # export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD

 export HCCL_IF_IP=$local_ip
@@ -505,7 +505,7 @@ local_ip="141.xx.xx.4"
 node0_ip="xxxx"

 # [Optional] jemalloc
-# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
+# jemalloc is for better performance, if `libjemalloc.so` is installed on your machine, you can turn it on.
 # export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD

 export HCCL_IF_IP=$local_ip
@@ -707,7 +707,7 @@ Run performance evaluation of `DeepSeek-V3.1-w8a8-mtp-QuaRot` as an example.

 Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.

-There are three `vllm bench` subcommand:
+There are three `vllm bench` subcommands:

 - `latency`: Benchmark the latency of a single batch of requests.
 - `serve`: Benchmark the online serving throughput.
--- a/docs/source/tutorials/DeepSeek-V3.2.md
+++ b/docs/source/tutorials/DeepSeek-V3.2.md
@@ -29,7 +29,7 @@ If you want to deploy multi-node environment, you need to verify multi-node comm

 ### Installation

-You can using our official docker image to run `DeepSeek-V3.2` directly..
+You can use our official docker image to run `DeepSeek-V3.2` directly.

 :::::{tab-set}
 :sync-group: install
@@ -456,11 +456,11 @@ Before you start, please
    dp_rpc_port = args.dp_rpc_port
    vllm_start_port = args.vllm_start_port

-    def run_command(visiable_devices, dp_rank, vllm_engine_port):
+    def run_command(visible_devices, dp_rank, vllm_engine_port):
        command = [
            "bash",
            "./run_dp_template.sh",
-            visiable_devices,
+            visible_devices,
            str(vllm_engine_port),
            str(dp_size),
            str(dp_rank),
@@ -481,9 +481,9 @@ Before you start, please
        for i in range(dp_size_local):
            dp_rank = dp_rank_start + i
            vllm_engine_port = vllm_start_port + i
-            visiable_devices = ",".join(str(x) for x in range(i * tp_size, (i + 1) * tp_size))
+            visible_devices = ",".join(str(x) for x in range(i * tp_size, (i + 1) * tp_size))
            process = multiprocessing.Process(target=run_command,
-                                            args=(visiable_devices, dp_rank,
+                                            args=(visible_devices, dp_rank,
                                                    vllm_engine_port))
            processes.append(process)
            process.start()
@@ -895,7 +895,7 @@ Run performance evaluation of `DeepSeek-V3.2-W8A8` as an example.

 Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.

-There are three `vllm bench` subcommand:
+There are three `vllm bench` subcommands:

 - `latency`: Benchmark the latency of a single batch of requests.
 - `serve`: Benchmark the online serving throughput.
--- a/docs/source/tutorials/GLM4.x.md
+++ b/docs/source/tutorials/GLM4.x.md
@@ -29,7 +29,7 @@ It is recommended to download the model weight to the shared directory of multip

 ### Installation

-You can using our official docker image to run `GLM-4.x` directly.
+You can use our official docker image to run `GLM-4.x` directly.

 Select an image based on your machine type and start the docker image on your node, refer to [using docker](../installation.md#set-up-using-docker).

@@ -146,7 +146,7 @@ Run performance evaluation of `GLM-4.x` as an example.

 Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.

-There are three `vllm bench` subcommand:
+There are three `vllm bench` subcommands:

 - `latency`: Benchmark the latency of a single batch of requests.
 - `serve`: Benchmark the online serving throughput.
--- a/docs/source/tutorials/PaddleOCR-VL.md
+++ b/docs/source/tutorials/PaddleOCR-VL.md
@@ -22,7 +22,7 @@ It is recommended to download the model weights to a local directory (e.g., `./P

 ### Installation

-You can using our official docker image to run `PaddleOCR-VL` directly.
+You can use our official docker image to run `PaddleOCR-VL` directly.

 Select an image based on your machine type and start the docker image on your node, refer to [using docker](../installation.md#set-up-using-docker).

--- a/docs/source/tutorials/Qwen-VL-Dense.md
+++ b/docs/source/tutorials/Qwen-VL-Dense.md
@@ -548,7 +548,7 @@ lm_eval \

 Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.

-There are three `vllm bench` subcommand:
+There are three `vllm bench` subcommands:

 - `latency`: Benchmark the latency of a single batch of requests.
 - `serve`: Benchmark the online serving throughput.
--- a/docs/source/tutorials/Qwen2.5-7B.md
+++ b/docs/source/tutorials/Qwen2.5-7B.md
@@ -24,7 +24,7 @@ It is recommended to download the model weights to a local directory (e.g., `./Q

 ### Installation

-You can using our official docker image and install extra operator for supporting `Qwen2.5-7B-Instruct`.
+You can use our official docker image and install extra operator for supporting `Qwen2.5-7B-Instruct`.

 :::::{tab-set}
 :sync-group: install
@@ -158,7 +158,7 @@ Run performance evaluation of `Qwen2.5-7B-Instruct` as an example.

 Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.

-There are three `vllm bench` subcommand:
+There are three `vllm bench` subcommands:

 - `latency`: Benchmark the latency of a single batch of requests.
 - `serve`: Benchmark the online serving throughput.
--- a/docs/source/tutorials/Qwen2.5-Omni.md
+++ b/docs/source/tutorials/Qwen2.5-Omni.md
@@ -19,11 +19,11 @@ Refer to [feature guide](../user_guide/feature_guide/index.md) to get the featur
 - `Qwen2.5-Omni-3B`(BF16): [Download model weight](https://huggingface.co/Qwen/Qwen2.5-Omni-3B)
 - `Qwen2.5-Omni-7B`(BF16): [Download model weight](https://huggingface.co/Qwen/Qwen2.5-Omni-7B)

-Following examples use the 7B version deafultly.
+Following examples use the 7B version by default.

 ### Installation

-You can using our official docker image to run `Qwen2.5-Omni` directly.
+You can use our official docker image to run `Qwen2.5-Omni` directly.

 Select an image based on your machine type and start the docker image on your node, refer to [using docker](../installation.md#set-up-using-docker).

@@ -97,7 +97,7 @@ VLLM_TARGET_DEVICE=empty pip install -v ".[audio]"

 `--allowed-local-media-path` is optional, only set it if you need infer model with local media file

-`--gpu-memory-utilization` should not be set manually only if yous know what this parameter aims to.
+`--gpu-memory-utilization` should not be set manually only if you know what this parameter aims to.

 #### Multiple NPU (Qwen2.5-Omni-7B)

@@ -195,7 +195,7 @@ Run performance evaluation of `Qwen2.5-Omni-7B` as an example.

 Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.

-There are three `vllm bench` subcommand:
+There are three `vllm bench` subcommands:

 - `latency`: Benchmark the latency of a single batch of requests.
 - `serve`: Benchmark the online serving throughput.
--- a/docs/source/tutorials/Qwen3-235B-A22B.md
+++ b/docs/source/tutorials/Qwen3-235B-A22B.md
@@ -300,7 +300,7 @@ Run performance evaluation of `Qwen3-235B-A22B-w8a8` as an example.

 Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.

-There are three `vllm bench` subcommand:
+There are three `vllm bench` subcommands:

 - `latency`: Benchmark the latency of a single batch of requests.
 - `serve`: Benchmark the online serving throughput.
--- a/docs/source/tutorials/Qwen3-Coder-30B-A3B.md
+++ b/docs/source/tutorials/Qwen3-Coder-30B-A3B.md
@@ -24,7 +24,7 @@ It is recommended to download the model weight to the shared directory of multip

 `Qwen3-Coder` is first supported in `vllm-ascend:v0.10.0rc1`, please run this model using a later version.

-You can using our official docker image to run `Qwen3-Coder-30B-A3B-Instruct` directly.
+You can use our official docker image to run `Qwen3-Coder-30B-A3B-Instruct` directly.

 ```{code-block} bash
   :substitutions:
--- a/docs/source/tutorials/Qwen3-Dense.md
+++ b/docs/source/tutorials/Qwen3-Dense.md
@@ -42,7 +42,7 @@ If you want to deploy multi-node environment, you need to verify multi-node comm

 ### Installation

-You can using our official docker image for supporting Qwen3 Dense models.
+You can use our official docker image for supporting Qwen3 Dense models.
 Currently, we provide the all-in-one images.[Download images](https://quay.io/repository/ascend/vllm-ascend?tab=tags)

 #### Docker Pull (by tag)
@@ -161,7 +161,7 @@ export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3
 export TASK_QUEUE_ENABLE=1

 # [Optional] jemalloc
-# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
+# jemalloc is for better performance, if `libjemalloc.so` is installed on your machine, you can turn it on.
 # if os is Ubuntu
 # export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
 # if os is openEuler
@@ -291,7 +291,7 @@ Run performance evaluation of `Qwen3-32B-W8A8` as an example.

 Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.

-There are three `vllm bench` subcommand:
+There are three `vllm bench` subcommands:

 - `latency`: Benchmark the latency of a single batch of requests.
 - `serve`: Benchmark the online serving throughput.
--- a/docs/source/tutorials/Qwen3-Next.md
+++ b/docs/source/tutorials/Qwen3-Next.md
@@ -154,7 +154,7 @@ Run performance evaluation of `Qwen3-Next` as an example.

 Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.

-There are three `vllm bench` subcommand:
+There are three `vllm bench` subcommands:

 - `latency`: Benchmark the latency of a single batch of requests.
 - `serve`: Benchmark the online serving throughput.
--- a/docs/source/tutorials/Qwen3-Omni-30B-A3B-Thinking.md
+++ b/docs/source/tutorials/Qwen3-Omni-30B-A3B-Thinking.md
@@ -24,7 +24,7 @@ It is recommended to download the model weight to the shared directory of multip
 :::::{tab-set}
 ::::{tab-item} Use docker image

-You can using our official docker image to run Qwen3-Omni-30B-A3B-Thinking directly
+You can use our official docker image to run Qwen3-Omni-30B-A3B-Thinking directly

 Select an image based on your machine type and start the docker image on your node, refer to [using docker](../installation.md#set-up-using-docker).

@@ -269,7 +269,7 @@ Run performance evaluation of `Qwen3-Omni-30B-A3B-Thinking` as an example.
 Refer to vllm benchmark for more details.
 Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.

-There are three `vllm bench` subcommand:
+There are three `vllm bench` subcommands:

 - `latency`: Benchmark the latency of a single batch of requests.
 - `serve`: Benchmark the online serving throughput.
--- a/docs/source/tutorials/Qwen3-VL-235B-A22B-Instruct.md
+++ b/docs/source/tutorials/Qwen3-VL-235B-A22B-Instruct.md
@@ -260,7 +260,7 @@ Run performance evaluation of `Qwen3-VL-235B-A22B-Instruct` as an example.

 Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.

-There are three `vllm bench` subcommand:
+There are three `vllm bench` subcommands:

 - `latency`: Benchmark the latency of a single batch of requests.
 - `serve`: Benchmark the online serving throughput.
--- a/docs/source/tutorials/long_sequence_context_parallel_multi_node.md
+++ b/docs/source/tutorials/long_sequence_context_parallel_multi_node.md
@@ -24,7 +24,7 @@ Refer to [verify multi-node communication environment](../installation.md#verify

 ### Installation

-You can using our official docker image to run `DeepSeek-V3.1` directly.
+You can use our official docker image to run `DeepSeek-V3.1` directly.

 Select an image based on your machine type and start the docker image on your node, refer to [using docker](../installation.md#set-up-using-docker).

@@ -351,7 +351,7 @@ Run performance evaluation of `DeepSeek-V3.1-w8a8` as an example.

 Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.

-There are three `vllm bench` subcommand:
+There are three `vllm bench` subcommands:

 - `latency`: Benchmark the latency of a single batch of requests.
 - `serve`: Benchmark the online serving throughput.
--- a/docs/source/tutorials/long_sequence_context_parallel_single_node.md
+++ b/docs/source/tutorials/long_sequence_context_parallel_single_node.md
@@ -159,7 +159,7 @@ Run performance evaluation of `Qwen3-235B-A22B-w8a8` as an example.

 Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.

-There are three `vllm bench` subcommand:
+There are three `vllm bench` subcommands:

 - `latency`: Benchmark the latency of a single batch of requests.
 - `serve`: Benchmark the online serving throughput.