[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/Qwen2.5-Omni.md

@@ -16,8 +16,8 @@ Refer to [feature guide](../../user_guide/feature_guide/index.md) to get the fea
 
 ### Model Weight
 
-- `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)
+- `Qwen2.5-Omni-3B`(BF16): [Download model weight](https://modelscope.cn/models/Qwen/Qwen2.5-Omni-3B)
+- `Qwen2.5-Omni-7B`(BF16): [Download model weight](https://modelscope.cn/models/Qwen/Qwen2.5-Omni-7B)
 
 Following examples use the 7B version by default.
 
@@ -71,6 +71,8 @@ docker run --rm \
 :::{note}
 The env `LOCAL_MEDIA_PATH` which allowing API requests to read local images or videos from directories specified by the server file system. Please note this is a security risk. Should only be enabled in trusted environments.
 
+:::
+
 ```bash
 export VLLM_USE_MODELSCOPE=true
 export MODEL_PATH="Qwen/Qwen2.5-Omni-7B"
@@ -104,10 +106,10 @@ VLLM_TARGET_DEVICE=empty pip install -v ".[audio]"
 ```bash
 export VLLM_USE_MODELSCOPE=true
 export MODEL_PATH=Qwen/Qwen2.5-Omni-7B
-export LOCAL_MEDIA_PATH=/local_path/to_media/
+export LOCAL_MEDIA_PATH=$HOME/.cache/vllm/assets/vllm_public_assets/
 export DP_SIZE=8
 
-vllm serve ${MODEL_PATH}\
+vllm serve ${MODEL_PATH} \
 --host 0.0.0.0 \
 --port 8000 \
 --served-model-name Qwen-Omni \
@@ -137,7 +139,7 @@ INFO:     Application startup complete.
 Once your server is started, you can query the model with input prompts:
 
 ```bash
-curl http://127.0.0.1:8000/v1/chat/completions   -H "Content-Type: application/json"   -H "Authorization: Bearer EMPTY"   -d '{
+curl http://localhost:8000/v1/chat/completions   -H "Content-Type: application/json"   -H "Authorization: Bearer EMPTY"   -d '{
     "model": "Qwen-Omni",
     "messages": [
       {