[Doc][releases/v0.18.0] fix documentation error or non-standard description (#8626)

### What this PR does / why we need it?
fix documentation error or non-standard description in releases/v0.18.0
branch
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Documentation check.

---------

Signed-off-by: linfeng-yuan <1102311262@qq.com>

benchmarks/README.md

@@ -132,12 +132,11 @@ Once the script completes, you can find the results in the benchmarks/results fo
 
 ```shell
 .
-|-- serving_qwen2_5_7B_tp1_qps_1.json
+|-- serving_qwen2_5_7Bvl_tp1_qps_1.json
-|-- serving_qwen2_5_7B_tp1_qps_16.json
+|-- serving_qwen2_5_7Bvl_tp1_qps_16.json
-|-- serving_qwen2_5_7B_tp1_qps_4.json
+|-- serving_qwen2_5_7Bvl_tp1_qps_4.json
-|-- serving_qwen2_5_7B_tp1_qps_inf.json
+|-- serving_qwen2_5_7Bvl_tp1_qps_inf.json
-|-- latency_qwen2_5_7B_tp1.json
+|-- throughput_qwen2_5_7Bvl_tp1.json
-|-- throughput_qwen2_5_7B_tp1.json
 ```
 
 These files contain detailed benchmarking results for further analysis.

docs/source/locale/zh_CN/LC_MESSAGES/tutorials/features/long_sequence_context_parallel_single_node.po

@@ -138,23 +138,23 @@ msgstr "**注意：**"
 #: ../../source/tutorials/features/long_sequence_context_parallel_single_node.md:108
 #, python-brace-format
 msgid ""
-"for vllm version below `v0.12.0` use parameter: `--rope_scaling "
+"for vllm version below `v0.12.0` use parameter: `--rope-scaling "
 "'{\"rope_type\":\"yarn\",\"factor\":4,\"original_max_position_embeddings\":32768}'"
 " \\`"
 msgstr ""
-"对于 vllm 版本低于 `v0.12.0`，使用参数：`--rope_scaling "
+"对于 vllm 版本低于 `v0.12.0`，使用参数：`--rope-scaling "
 "'{\"rope_type\":\"yarn\",\"factor\":4,\"original_max_position_embeddings\":32768}'"
 " \\`"
 
 #: ../../source/tutorials/features/long_sequence_context_parallel_single_node.md:109
 #, python-brace-format
 msgid ""
-"for vllm version `v0.12.0` use parameter: `--hf-overrides "
+"for vllm version same as or newer than `v0.12.0` use parameter: `--hf-overrides "
 "'{\"rope_parameters\": "
 "{\"rope_type\":\"yarn\",\"rope_theta\":1000000,\"factor\":4,\"original_max_position_embeddings\":32768}}'"
 " \\`"
 msgstr ""
-"对于 vllm 版本 `v0.12.0`，使用参数：`--hf-overrides '{\"rope_parameters\": "
+"对于 vllm 版本 `v0.12.0`及以上，使用参数：`--hf-overrides '{\"rope_parameters\": "
 "{\"rope_type\":\"yarn\",\"rope_theta\":1000000,\"factor\":4,\"original_max_position_embeddings\":32768}}'"
 " \\`"
 

docs/source/locale/zh_CN/LC_MESSAGES/tutorials/models/Qwen2.5-Omni.po

@@ -210,8 +210,8 @@ msgstr "详情请参考[使用 AISBench](../../developer_guide/evaluation/using_
 #: ../../source/tutorials/models/Qwen2.5-Omni.md:181
 msgid ""
 "After execution, you can get the result, here is the result of `Qwen2.5"
-"-Omni-7B` with `vllm-ascend:0.11.0rc0` for reference only."
+"-Omni-7B` with `vllm-ascend:v0.11.0rc0` for reference only."
-msgstr "执行后，您可以获得结果，以下是 `Qwen2.5-Omni-7B` 在 `vllm-ascend:0.11.0rc0` 上的结果，仅供参考。"
+msgstr "执行后，您可以获得结果，以下是 `Qwen2.5-Omni-7B` 在 `vllm-ascend:v0.11.0rc0` 上的结果，仅供参考。"
 
 #: ../../source/tutorials/models/Qwen2.5-Omni.md:91
 msgid "dataset"

docs/source/locale/zh_CN/LC_MESSAGES/tutorials/models/Qwen3-235B-A22B.po

@@ -218,14 +218,14 @@ msgstr ""
 #: ../../source/tutorials/models/Qwen3-235B-A22B.md:130
 #, python-brace-format
 msgid ""
-"For vllm version below `v0.12.0`, use parameter: `--rope_scaling "
+"For vllm version below `v0.12.0`, use parameter: `--rope-scaling "
 "'{\"rope_type\":\"yarn\",\"factor\":4,\"original_max_position_embeddings\":32768}'"
 " \\`. If you are using weights like [Qwen3-235B-A22B-"
 "Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507)"
 " which originally supports long contexts, there is no need to add this "
 "parameter."
 msgstr ""
-"对于 `v0.12.0` 以下版本的 vLLM，使用参数：`--rope_scaling "
+"对于 `v0.12.0` 以下版本的 vLLM，使用参数：`--rope-scaling "
 "'{\"rope_type\":\"yarn\",\"factor\":4,\"original_max_position_embeddings\":32768}'"
 " \\`。如果您使用的是像 [Qwen3-235B-A22B-"
 "Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507)"
@@ -452,8 +452,8 @@ msgstr "详情请参阅 [使用 AISBench](../../developer_guide/evaluation/using
 #: ../../source/tutorials/models/Qwen3-235B-A22B.md:285
 msgid ""
 "After execution, you can get the result, here is the result of `Qwen3"
-"-235B-A22B-w8a8` in `vllm-ascend:0.11.0rc0` for reference only."
+"-235B-A22B-w8a8` in `vllm-ascend:v0.11.0rc0` for reference only."
-msgstr "执行后，您将获得结果。以下是 `vllm-ascend:0.11.0rc0` 中 `Qwen3-235B-A22B-w8a8` 的结果，仅供参考。"
+msgstr "执行后，您将获得结果。以下是 `vllm-ascend:v0.11.0rc0` 中 `Qwen3-235B-A22B-w8a8` 的结果，仅供参考。"
 
 #: ../../source/tutorials/models/Qwen3-235B-A22B.md:76
 msgid "dataset"

docs/source/locale/zh_CN/LC_MESSAGES/tutorials/models/Qwen3-Coder-30B-A3B.po

@@ -155,8 +155,8 @@ msgstr "详情请参考[使用 AISBench](../../developer_guide/evaluation/using_
 #: ../../source/tutorials/models/Qwen3-Coder-30B-A3B.md:95
 msgid ""
 "After execution, you can get the result, here is the result of `Qwen3"
-"-Coder-30B-A3B-Instruct` in `vllm-ascend:0.11.0rc0` for reference only."
+"-Coder-30B-A3B-Instruct` in `vllm-ascend:v0.11.0rc0` for reference only."
-msgstr "执行后，您可以获得结果。以下是 `Qwen3-Coder-30B-A3B-Instruct` 在 `vllm-ascend:0.11.0rc0` 中的结果，仅供参考。"
+msgstr "执行后，您可以获得结果。以下是 `Qwen3-Coder-30B-A3B-Instruct` 在 `vllm-ascend:v0.11.0rc0` 中的结果，仅供参考。"
 
 #: ../../source/tutorials/models/Qwen3-Coder-30B-A3B.md:29
 msgid "dataset"

docs/source/tutorials/features/long_sequence_context_parallel_single_node.md

@@ -105,8 +105,8 @@ vllm serve vllm-ascend/Qwen3-235B-A22B-w8a8 \
 
 **Notice:**
 
-- for vllm version below `v0.12.0` use parameter: `--rope_scaling '{"rope_type":"yarn","factor":4,"original_max_position_embeddings":32768}' \`
+- for vllm version below `v0.12.0` use parameter: `--rope-scaling '{"rope_type":"yarn","factor":4,"original_max_position_embeddings":32768}' \`
-- for vllm version `v0.12.0` use parameter: `--hf-overrides '{"rope_parameters": {"rope_type":"yarn","rope_theta":1000000,"factor":4,"original_max_position_embeddings":32768}}' \`
+- for vllm version same as or newer than `v0.12.0` use parameter: `--hf-overrides '{"rope_parameters": {"rope_type":"yarn","rope_theta":1000000,"factor":4,"original_max_position_embeddings":32768}}' \`
 
 The parameters are explained as follows:
 

docs/source/tutorials/features/pd_disaggregation_mooncake_single_node.md

@@ -244,7 +244,7 @@ python load_balance_proxy_server_example.py \
     --host 192.0.0.1 \
     --port 8080 \
     --prefiller-hosts 192.0.0.1 \
-    --prefiller-port 13700 \
+    --prefiller-ports 13700 \
     --decoder-hosts 192.0.0.1 \
     --decoder-ports 13701
 ```
@@ -252,7 +252,7 @@ python load_balance_proxy_server_example.py \
 |Parameter  | Meaning |
 | --- | --- |
 | --port | Port of proxy |
-| --prefiller-port | All ports of prefill |
+| --prefiller-ports | All ports of prefill |
 | --decoder-ports | All ports of decoder |
 
 ## Verification

docs/source/tutorials/models/Qwen2.5-Omni.md

@@ -178,7 +178,7 @@ Qwen2.5-Omni on vllm-ascend has been tested on AISBench.
 
 1. Refer to [Using AISBench](../../developer_guide/evaluation/using_ais_bench.md) for details.
 
-2. After execution, you can get the result, here is the result of `Qwen2.5-Omni-7B` with `vllm-ascend:0.11.0rc0` for reference only.
+2. After execution, you can get the result, here is the result of `Qwen2.5-Omni-7B` with `vllm-ascend:v0.11.0rc0` for reference only.
 
 | dataset | platform | metric | mode | vllm-api-stream-chat |
 |----- | ----- | ----- | ----- | -----|

docs/source/tutorials/models/Qwen3-235B-A22B.md

@@ -127,7 +127,7 @@ vllm serve vllm-ascend/Qwen3-235B-A22B-w8a8 \
 
 - [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B#processing-long-texts) originally only supports 40960 context(max_position_embeddings). If you want to use it and its related quantization weights to run long seqs (such as 128k context), it is required to use yarn rope-scaling technique.
     - For vLLM version same as or new than `v0.12.0`, use parameter: `--hf-overrides '{"rope_parameters": {"rope_type":"yarn","rope_theta":1000000,"factor":4,"original_max_position_embeddings":32768}}' \`.
-    - For vllm version below `v0.12.0`, use parameter: `--rope_scaling '{"rope_type":"yarn","factor":4,"original_max_position_embeddings":32768}' \`.
+    - For vllm version below `v0.12.0`, use parameter: `--rope-scaling '{"rope_type":"yarn","factor":4,"original_max_position_embeddings":32768}' \`.
   If you are using weights like [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507) which originally supports long contexts, there is no need to add this parameter.
 
 The parameters are explained as follows:
@@ -150,7 +150,7 @@ The parameters are explained as follows:
 
 ### Multi-node Deployment with MP (Recommended)
 
-Assume you have Atlas 800 A3 (64G*16) nodes (or 2* A2), and want to deploy the `Qwen3-VL-235B-A22B-Instruct` model across multiple nodes.
+Assume you have Atlas 800 A3 (64G*16) nodes (or 2* A2), and want to deploy the `Qwen3-235B-A22B-Instruct` model across multiple nodes.
 
 Node 0
 
@@ -282,7 +282,7 @@ Here are two accuracy evaluation methods.
 
 1. Refer to [Using AISBench](../../developer_guide/evaluation/using_ais_bench.md) for details.
 
-2. After execution, you can get the result, here is the result of `Qwen3-235B-A22B-w8a8` in `vllm-ascend:0.11.0rc0` for reference only.
+2. After execution, you can get the result, here is the result of `Qwen3-235B-A22B-w8a8` in `vllm-ascend:v0.11.0rc0` for reference only.
 
 | dataset | version | metric | mode | vllm-api-general-chat |
 |----- | ----- | ----- | ----- | -----|
@@ -310,7 +310,7 @@ Take the `serve` as an example. Run the code as follows.
 
 ```shell
 export VLLM_USE_MODELSCOPE=true
-vllm bench serve --model vllm-ascend/Qwen3-235B-A22B-w8a8  --dataset-name random --random-input 200 --num-prompts 200 --request-rate 1 --save-result --result-dir ./
+vllm bench serve --model vllm-ascend/Qwen3-235B-A22B-w8a8  --dataset-name random --random-input-len 200 --num-prompts 200 --request-rate 1 --save-result --result-dir ./
 ```
 
 After about several minutes, you can get the performance evaluation result.
@@ -589,7 +589,7 @@ vllm serve vllm-ascend/Qwen3-235B-A22B-w8a8 \
 PD proxy:
 
 ```shell
-python load_balance_proxy_server_example.py --port 12347 --prefiller-hosts prefill_node_1_ip --prefiller-port 8000 --decoder-hosts decode_node_1_ip --decoder-ports 8000
+python load_balance_proxy_server_example.py --port 12347 --prefiller-hosts prefill_node_1_ip --prefiller-ports 8000 --decoder-hosts decode_node_1_ip --decoder-ports 8000
 ```
 
 Benchmark scripts:

docs/source/tutorials/models/Qwen3-32B-W4A4.md

@@ -108,10 +108,10 @@ curl http://localhost:8000/v1/completions \
     -d '{
         "model": "qwen3-32b-w4a4",
         "prompt": "what is large language model?",
-        "max_completion_tokens": "128",
+        "max_completion_tokens": 128,
-        "top_p": "0.95",
+        "top_p": 0.95,
-        "top_k": "40",
+        "top_k": 40,
-        "temperature": "0.0"
+        "temperature": 0
     }'
 ```
 

docs/source/tutorials/models/Qwen3-8B-W4A8.md

@@ -106,10 +106,10 @@ curl http://localhost:8000/v1/completions \
     -d '{
         "model": "qwen3-8b-w4a8",
         "prompt": "what is large language model?",
-        "max_completion_tokens": "128",
+        "max_completion_tokens": 128,
-        "top_p": "0.95",
+        "top_p": 0.95,
-        "top_k": "40",
+        "top_k": 40,
-        "temperature": "0.0"
+        "temperature": 0
     }'
 ```
 

docs/source/tutorials/models/Qwen3-Coder-30B-A3B.md

@@ -92,7 +92,7 @@ curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/jso
 
 1. Refer to [Using AISBench](../../developer_guide/evaluation/using_ais_bench.md) for details.
 
-2. After execution, you can get the result, here is the result of `Qwen3-Coder-30B-A3B-Instruct` in `vllm-ascend:0.11.0rc0` for reference only.
+2. After execution, you can get the result, here is the result of `Qwen3-Coder-30B-A3B-Instruct` in `vllm-ascend:v0.11.0rc0` for reference only.
 
 | dataset | version | metric | mode | vllm-api-general-chat |
 |----- | ----- | ----- | ----- | -----|

docs/source/tutorials/models/Qwen3-VL-Reranker.md

@@ -71,7 +71,7 @@ Start the server with the following command:
 vllm serve Qwen/Qwen3-VL-Reranker-8B \
     --runner pooling \
     --max-model-len 4096 \
-    --hf_overrides '{"architectures": ["Qwen3VLForSequenceClassification"],"classifier_from_token": ["no", "yes"],"is_original_qwen3_reranker": true}' \
+    --hf-overrides '{"architectures": ["Qwen3VLForSequenceClassification"],"classifier_from_token": ["no", "yes"],"is_original_qwen3_reranker": true}' \
     --chat-template ./qwen3_vl_reranker.jinja
 ```
 

docs/source/tutorials/models/Qwen3_reranker.md

@@ -35,7 +35,7 @@ Using the Qwen3-Reranker-8B model as an example, first run the docker container
 ### Online Inference
 
 ```bash
-vllm serve Qwen/Qwen3-Reranker-8B --host 127.0.0.1 --port 8888 --hf_overrides '{"architectures": ["Qwen3ForSequenceClassification"],"classifier_from_token": ["no", "yes"],"is_original_qwen3_reranker": true}'
+vllm serve Qwen/Qwen3-Reranker-8B --host 127.0.0.1 --port 8888 --hf-overrides '{"architectures": ["Qwen3ForSequenceClassification"],"classifier_from_token": ["no", "yes"],"is_original_qwen3_reranker": true}'
 ```
 
 Once your server is started, you can send request with follow examples.

tests/e2e/multicard/2-cards/test_qwen3_performance.py

@@ -42,7 +42,7 @@ vllm_bench_cases = {
     "random_input_len": 128,
     "max_concurrency": 40,
     "random_output_len": 100,
-    "temperature": 0.0,
+    "temperature": 0,
 }
 
 # NOTE: Any changes for the baseline throughput should be approved by team members.