Add Qwen3-Next tutorials

docs/source/tutorials/multi_npu_qwen3_next.md → docs/source/tutorials/Qwen3-Next.md

@@ -1,15 +1,27 @@
-# Multi-NPU (Qwen3-Next)
+# Qwen3-Next
 
-```{note}
-The Qwen3 Next is using [Triton Ascend](https://gitee.com/ascend/triton-ascend) which is currently experimental. In future versions, there may be behavioral changes related to stability, accuracy, and performance improvement.
-```
+## Introduction
+
+The Qwen3-Next model is a sparse MoE (Mixture of Experts) model with high sparsity. Compared to the MoE architecture of Qwen3, it has introduced key improvements in aspects such as the hybrid attention mechanism and multi-token prediction mechanism, enhancing the training and inference efficiency of the model under long contexts and large total parameter scales.
+
+This document will present the core verification steps of the model, including supported features, environment preparation, as well as accuracy and performance evaluation. Qwen3 Next is currently using Triton Ascend, which is in the experimental phase. In subsequent versions, its performance related to stability and accuracy may change, and performance will be continuously optimized.
+
+The `Qwen3-Next` model is first supported in `vllm-ascend:v0.10.2rc1`.
+
+## Supported Features
+
+Refer to [supported features](../user_guide/support_matrix/supported_models.md) to get the model's supported feature matrix.
+
+Refer to [feature guide](../user_guide/feature_guide/index.md) to get the feature's configuration.
 
-## Run vllm-ascend on Multi-NPU with Qwen3 Next
+## Weight Preparation
 
-Run docker container:
+ Download Link for the `Qwen3-Next-80B-A3B-Instruct` Model Weights: [Download model weight](https://modelers.cn/models/Modelers_Park/Qwen3-Next-80B-A3B-Instruct/tree/main)
+
+## Deployment
+### Run docker container
 
 ```{code-block} bash
-   :substitutions:
 # Update the vllm-ascend image
 export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
 docker run --rm \
@@ -32,12 +44,7 @@ docker run --rm \
 -it $IMAGE bash
 ```
 
-Set up environment variables:
-
-```bash
-# Load model from ModelScope to speed up download
-export VLLM_USE_MODELSCOPE=True
-```
+The Qwen3 Next is using [Triton Ascend](https://gitee.com/ascend/triton-ascend) which is currently experimental. In future versions, there may be behavioral changes related to stability, accuracy, and performance improvement.
 
 ### Install Triton Ascend
 
@@ -49,14 +56,17 @@ The [Triton Ascend](https://gitee.com/ascend/triton-ascend) is required when you
 Install the Ascend BiSheng toolkit:
 
 ```bash
-source /usr/local/Ascend/ascend-toolkit/8.3.RC2/bisheng_toolkit/set_env.sh
+wget https://vllm-ascend.obs.cn-north-4.myhuaweicloud.com/vllm-ascend/Ascend-BiSheng-toolkit_aarch64.run
+chmod a+x Ascend-BiSheng-toolkit_aarch64.run
+./Ascend-BiSheng-toolkit_aarch64.run --install
+source /usr/local/Ascend/8.3.RC1/bisheng_toolkit/set_env.sh
 ```
 
 Install Triton Ascend:
 
 ```bash
-wget https://vllm-ascend.obs.cn-north-4.myhuaweicloud.com/vllm-ascend/triton_ascend-3.2.0.dev2025110717-cp311-cp311-manylinux_2_27_aarch64.whl
-pip install triton_ascend-3.2.0.dev2025110717-cp311-cp311-manylinux_2_27_aarch64.whl
+wget https://vllm-ascend.obs.cn-north-4.myhuaweicloud.com/vllm-ascend/triton_ascend-3.2.0.dev20250914-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
+pip install triton_ascend-3.2.0.dev20250914-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
 ```
 
 ::::
@@ -68,13 +78,7 @@ Coming soon ...
 ::::
 :::::
 
-### Inference on Multi-NPU
-
-Please make sure you have already executed the command:
-
-```bash
-source /usr/local/Ascend/ascend-toolkit/8.3.RC2/bisheng_toolkit/set_env.sh
-```
+### Inference
 
 :::::{tab-set}
 ::::{tab-item} Online Inference
@@ -152,3 +156,35 @@ Prompt: 'Who are you?', Generated text: ' What do you know about me?\n\nHello! I
 
 ::::
 :::::
+
+## Accuracy Evaluation
+
+### Using AISBench
+
+ Refer to [Using AISBench](../developer_guide/evaluation/using_ais_bench.md) for details.
+
+## Performance
+
+### Using AISBench
+
+Refer to [Using AISBench for performance evaluation](../developer_guide/evaluation/using_ais_bench.md#execute-performance-evaluation) for details.
+
+### Using vLLM Benchmark
+
+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:
+- `latency`: Benchmark the latency of a single batch of requests.
+- `serve`: Benchmark the online serving throughput.
+- `throughput`: Benchmark offline inference throughput.
+
+Take the `serve` as an example. Run the code as follows.
+
+```shell
+export VLLM_USE_MODELSCOPE=true
+vllm bench serve --model Qwen/Qwen3-Next-80B-A3B-Instruct  --dataset-name random --random-input 200 --num-prompt 200 --request-rate 1 --save-result --result-dir ./
+```
+
+After about several minutes, you can get the performance evaluation result.

docs/source/tutorials/index.md

@@ -10,7 +10,7 @@ single_npu_qwen3_embedding
 single_npu_qwen3_quantization
 single_npu_qwen3_w4a4
 single_node_pd_disaggregation_llmdatadist
-multi_npu_qwen3_next
+Qwen3-Next
 multi_npu
 multi_npu_moge
 multi_npu_qwen3_moe