[Tutorial] Add qwen3 8b w4a8 tutorial (#2249)

### What this PR does / why we need it?

Add a new single npu quantization tutorial, and using the latest qwen3
model.

- vLLM version: v0.10.0
- vLLM main:
8e8e0b6af1

Signed-off-by: 22dimensions <waitingwind@foxmail.com>

docs/source/tutorials/index.md

@@ -7,6 +7,7 @@ single_npu
 single_npu_multimodal
 single_npu_audio
 single_npu_qwen3_embedding
+single_npu_qwen3_quantization
 multi_npu
 multi_npu_moge
 multi_npu_qwen3_moe

docs/source/tutorials/single_npu_qwen3_quantization.md

@@ -0,0 +1,131 @@
+# Single-NPU (Qwen3 8B W4A8)
+
+## Run docker container
+:::{note}
+w4a8 quantization feature is supported by v0.9.1rc2 or higher
+:::
+
+```{code-block} bash
+   :substitutions:
+# Update the vllm-ascend image
+export IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:|vllm_ascend_version|
+docker run --rm \
+--name vllm-ascend \
+--device /dev/davinci0 \
+--device /dev/davinci_manager \
+--device /dev/devmm_svm \
+--device /dev/hisi_hdc \
+-v /usr/local/dcmi:/usr/local/dcmi \
+-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
+-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 /root/.cache:/root/.cache \
+-p 8000:8000 \
+-it $IMAGE bash
+```
+
+## Install modelslim and convert model
+:::{note}
+You can choose to convert the model yourself or use the quantized model we uploaded,
+see https://www.modelscope.cn/models/vllm-ascend/Qwen3-8B-W4A8
+:::
+
+```bash
+# Optional, this commit has been verified
+git clone https://gitee.com/ascend/msit -b f8ab35a772a6c1ee7675368a2aa4bafba3bedd1a
+
+cd msit/msmodelslim
+# Install by run this script
+bash install.sh
+
+cd example/Qwen
+# Original weight path, Replace with your local model path
+MODEL_PATH=/home/models/Qwen3-8B
+# Path to save converted weight, Replace with your local path
+SAVE_PATH=/home/models/Qwen3-8B-w4a8
+
+python quant_qwen.py \
+          --model_path $MODEL_PATH \
+          --save_directory $SAVE_PATH \
+          --device_type npu \
+          --model_type qwen3 \
+          --calib_file None \
+          --anti_method m6 \
+          --anti_calib_file ./calib_data/mix_dataset.json \
+          --w_bit 4 \
+          --a_bit 8 \
+          --is_lowbit True \
+          --open_outlier False \
+          --group_size 256 \
+          --is_dynamic True \
+          --trust_remote_code True \
+          --w_method HQQ
+```
+
+## Verify the quantized model
+The converted model files looks like:
+
+```bash
+.
+|-- config.json
+|-- configuration.json
+|-- generation_config.json
+|-- merges.txt
+|-- quant_model_description.json
+|-- quant_model_weight_w4a8_dynamic-00001-of-00003.safetensors
+|-- quant_model_weight_w4a8_dynamic-00002-of-00003.safetensors
+|-- quant_model_weight_w4a8_dynamic-00003-of-00003.safetensors
+|-- quant_model_weight_w4a8_dynamic.safetensors.index.json
+|-- README.md
+|-- tokenizer.json
+`-- tokenizer_config.json
+```
+
+Run the following script to start the vLLM server with quantized model:
+
+```bash
+vllm serve /home/models/Qwen3-8B-w4a8 --served-model-name "qwen3-8b-w4a8" --max-model-len 4096 --quantization ascend
+```
+
+Once your server is started, you can query the model with input prompts
+
+```bash
+curl http://localhost:8000/v1/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "qwen3-8b-w4a8",
+        "prompt": "what is large language model?",
+        "max_tokens": "128",
+        "top_p": "0.95",
+        "top_k": "40",
+        "temperature": "0.0"
+    }'
+```
+
+Run the following script to execute offline inference on Single-NPU with quantized model:
+
+:::{note}
+To enable quantization for ascend, quantization method must be "ascend"
+:::
+
+```python
+
+from vllm import LLM, SamplingParams
+
+prompts = [
+    "Hello, my name is",
+    "The future of AI is",
+]
+sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40)
+
+llm = LLM(model="/home/models/Qwen3-8B-w4a8",
+          max_model_len=4096,
+          quantization="ascend")
+
+outputs = llm.generate(prompts, sampling_params)
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```