[Doc]Refresh model tutorial examples and serving commands (#7426)

### What this PR does / why we need it?
Main updates include:
- update model IDs and default model paths in serving / offline
inference examples

- adjust some command snippets and notes for better copy-paste usability

- replace `SamplingParams` argument usage from `max_completion_tokens`
to `max_tokens`（**Offline** inference currently **does not support** the
"max_completion_tokens"）
``` bash
Traceback (most recent call last):
  File "/vllm-workspace/vllm-ascend/qwen-next.py", line 18, in <module>
    sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40, max_completion_tokens=32)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: Unexpected keyword argument 'max_completion_tokens'
[ERROR] 2026-03-17-09:57:40 (PID:276, Device:-1, RankID:-1) ERR99999 UNKNOWN applicaiton exception
```

- refresh **Qwen3-Omni-30B-A3B-Thinking** recommended environment
variable
``` bash
export HCCL_BUFFSIZE=512
export HCCL_OP_EXPANSION_MODE=AIV
```
``` bash
EZ9999[PID: 25038] 2026-03-17-08:21:12.001.372 (EZ9999):  HCCL_BUFFSIZE is too SMALL, maxBs = 256, h = 2048, 
epWorldSize = 2, localMoeExpertNum = 64, sharedExpertNum = 0, tokenNeedSizeDispatch = 4608, tokenNeedSizeCombine 
= 4096, k = 8, NEEDED_HCCL_BUFFSIZE(((maxBs * tokenNeedSizeDispatch * ep_worldsize * localMoeExpertNum) + 
(maxBs * tokenNeedSizeCombine * (k + sharedExpertNum))) * 2) = 305MB, HCCL_BUFFSIZE=200MB.
[FUNC:CheckWinSize][FILE:moe_distribute_dispatch_v2_tiling.cpp][LINE:984]
```

- fix **Qwen3-reranker** example usage to match the current **pooling
runner** interface and score output access
``` python
model = LLM(
    model=model_name,
    task="score",       # need fix
    hf_overrides={
        "architectures": ["Qwen3ForSequenceClassification"],
        "classifier_from_token": ["no", "yes"],
```
--->
``` python
model = LLM(
    model=model_name,
    runner="pooling",
    hf_overrides={
        "architectures": ["Qwen3ForSequenceClassification"],
        "classifier_from_token": ["no", "yes"],
```

- modify **PaddleOCR-VL**  parameter `TASK_QUEUE_ENABLE` from `2` to `1`
``` bash
(EngineCore_DP0 pid=26273) RuntimeError: NPUModelRunner init failed, error is NPUModelRunner failed, error
 is Do not support TASK_QUEUE_ENABLE = 2 during NPU graph capture, please export TASK_QUEUE_ENABLE=1/0.
```

These changes are needed because several documentation examples had
drifted from the current runtime behavior and recommended invocation
patterns, which could confuse users when following the tutorials
directly.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?

- vLLM version: v0.17.0
- vLLM main:
4497431df6

Signed-off-by: MrZ20 <2609716663@qq.com>

docs/source/tutorials/models/Qwen3-Dense.md

@@ -8,11 +8,7 @@ Welcome to the tutorial on optimizing Qwen Dense models in the vLLM-Ascend envir
 
 This document will show the main verification steps of the model, including supported features, feature configuration, environment preparation, accuracy and performance evaluation.
 
-The Qwen3 Dense models is first supported in [v0.8.4rc2](https://github.com/vllm-project/vllm-ascend/blob/main/docs/source/user_guide/release_notes.md#v084rc2---20250429)
-
-## **Node**
-
-This example requires version **v0.11.0rc2**. Earlier versions may lack certain features.
+The Qwen3 Dense models is first supported in [v0.8.4rc2](https://github.com/vllm-project/vllm-ascend/blob/main/docs/source/user_guide/release_notes.md#v084rc2---20250429). This example requires version **v0.11.0rc2**. Earlier versions may lack certain features.
 
 ## Supported Features
 
@@ -115,12 +111,13 @@ The specific example scenario is as follows:
 
 ### Run docker container
 
-#### **Node**
+:::{note}
 
-- /model/Qwen3-32B-W8A8 is the model path, replace this with your actual path.
+- vllm-ascend/Qwen3-32B-W8A8 is the default model path, replace this with your actual path.
 - v0.11.0rc2-a3 is image tag, replace this with your actual tag.
 - replace this with your actual port: '-p 8113:8113'.
 - replace this with your actual card: '--device /dev/davinci0'.
+:::
 
 ```{code-block} bash
    :substitutions:
@@ -142,7 +139,7 @@ docker run --rm \
 -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
 -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
 -v /etc/ascend_install.info:/etc/ascend_install.info \
--v /model/Qwen3-32B-W8A8:/model/Qwen3-32B-W8A8 \
+-v /root/.cache:/root/.cache \
 -p 8113:8113 \
 -it $IMAGE bash
 ```
@@ -174,7 +171,7 @@ export HCCL_OP_EXPANSION_MODE="AIV"
 # Enable FlashComm_v1 optimization when tensor parallel is enabled.
 export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
 
-vllm serve /model/Qwen3-32B-W8A8 \
+vllm serve vllm-ascend/Qwen3-32B-W8A8 \
   --served-model-name qwen3 \
   --trust-remote-code \
   --async-scheduling \
@@ -190,15 +187,16 @@ vllm serve /model/Qwen3-32B-W8A8 \
   --gpu-memory-utilization 0.9
 ```
 
-#### **Node**
+:::{note}
 
-- /model/Qwen3-32B-W8A8 is the model path, replace this with your actual path.
+- vllm-ascend/Qwen3-32B-W8A8 is the default model path, replace this with your actual path.
 
 - If the model is not a quantized model, remove the `--quantization ascend` parameter.
 
 - **[Optional]** `--additional-config '{"pa_shape_list":[48,64,72,80]}'`: `pa_shape_list` specifies the batch sizes where you want to switch to the PA operator. This is a temporary tuning knob. Currently, the attention operator dispatch defaults to the FIA operator. In some batch-size (concurrency) settings, FIA may have suboptimal performance. By setting `pa_shape_list`, when the runtime batch size matches one of the listed values, vLLM-Ascend will replace FIA with the PA operator to prevent performance degradation. In the future, FIA will be optimized for these scenarios and this parameter will be removed.
 
 - If the ultimate performance is desired, the cudagraph_capture_sizes parameter can be enabled, reference: [key-optimization-points](./Qwen3-Dense.md#key-optimization-points)、[optimization-highlights](./Qwen3-Dense.md#optimization-highlights). Here is an example of batchsize of 72: `--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY", "cudagraph_capture_sizes":[1,8,24,48,60,64,72,76]}'`.
+:::
 
 Once your server is started, you can query the model with input prompts
 
@@ -219,11 +217,12 @@ curl http://localhost:8113/v1/chat/completions -H "Content-Type: application/jso
 
 Run the following script to execute offline inference on multi-NPU.
 
-#### **Node**
+:::{note}
 
-- /model/Qwen3-32B-W8A8 is the model path, replace this with your actual path.
+- vllm-ascend/Qwen3-32B-W8A8 is the default model path, replace this with your actual path.
 
 - If the model is not a quantized model,remove the `quantization="ascend"` parameter.
+:::
 
 ```python
 import gc
@@ -244,7 +243,7 @@ prompts = [
     "The future of AI is",
 ]
 sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40)
-llm = LLM(model="/model/Qwen3-32B-W8A8",
+llm = LLM(model="vllm-ascend/Qwen3-32B-W8A8",
           tensor_parallel_size=4,
           trust_remote_code=True,
           distributed_executor_backend="mp",
@@ -299,12 +298,13 @@ There are three `vllm bench` subcommands:
 
 Take the `serve` as an example. Run the code as follows.
 
-#### **Node**
+:::{note}
 
-- /model/Qwen3-32B-W8A8 is the model path, replace this with your actual path.
+- vllm-ascend/Qwen3-32B-W8A8 is the default model path, replace this with your actual path.
+:::
 
 ```shell
-vllm bench serve --model /model/Qwen3-32B-W8A8 --served-model-name qwen3 --port 8113 --dataset-name random --random-input 200 --num-prompts 200 --request-rate 1 --save-result --result-dir ./
+vllm bench serve --model vllm-ascend/Qwen3-32B-W8A8 --served-model-name qwen3 --port 8113 --dataset-name random --random-input 200 --num-prompts 200 --request-rate 1 --save-result --result-dir ./
 ```
 
 After about several minutes, you can get the performance evaluation result.