diff --git a/docs/source/tutorials/Qwen2.5.md b/docs/source/tutorials/Qwen2.5.md
new file mode 100644
index 00000000..2555e950
--- /dev/null
+++ b/docs/source/tutorials/Qwen2.5.md
@@ -0,0 +1,177 @@
+# Qwen2.5-7B-Instruct
+
+## Introduction
+
+Qwen2.5-7B-Instruct is the flagship instruction-tuned variant of Alibaba Cloud’s Qwen 2.5 LLM series. It supports a maximum context window of 128K, enables generation of up to 8K tokens, and delivers enhanced capabilities in multilingual processing, instruction following, programming, mathematical computation, and structured data handling.
+
+This document details the complete deployment and verification workflow for the model, including supported features, environment preparation, single-node deployment, functional verification, accuracy and performance evaluation, and troubleshooting of common issues. It is designed to help users quickly complete model deployment and validation.
+
+The `Qwen2.5-7B-Instruct` model was supported since `vllm-ascend:v0.9.0`.
+
+## 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.
+
+## Environment Preparation
+
+### Model Weight
+
+- `Qwen2.5-7B-Instruct`(BF16 version): require 1 910B4 cards(32G × 1). [Qwen2.5-7B-Instruct](https://modelscope.cn/models/Qwen/Qwen2.5-7B-Instruct)
+
+It is recommended to download the model weights to a local directory (e.g., `./Qwen2.5-7B-Instruct/`) for quick access during deployment.
+
+### Installation
+
+You can using our official docker image and install extra operator for supporting `Qwen2.5-7B-Instruct`.
+
+:::::{tab-set}
+:sync-group: install
+
+::::{tab-item} A3 series
+:sync: A3
+
+1. Start the docker image on your each node.
+
+```{code-block} bash
+   :substitutions:
+
+export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-a3
+docker run --rm \
+    --name vllm-ascend \
+    --shm-size=1g \
+    --net=host \
+    --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/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
+    -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 \
+    -it $IMAGE bash
+```
+
+::::
+::::{tab-item} A2 series
+:sync: A2
+
+Start the docker image on your each node.
+
+```{code-block} bash
+   :substitutions:
+
+export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
+docker run --rm \
+    --name vllm-ascend \
+    --shm-size=1g \
+    --net=host \
+    --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/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
+    -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 \
+    -it $IMAGE bash
+```
+
+::::
+:::::
+
+## Deployment
+
+### Single-node Deployment
+
+Qwen2.5-7B-Instruct supports single-node single-card deployment on the 910B4 platform. Follow these steps to start the inference service:
+
+1. Prepare model weights: Ensure the downloaded model weights are stored in the `./Qwen2.5-7B-Instruct/` directory.
+2. Create and execute the deployment script (save as `deploy.sh`):
+
+```shell
+#!/bin/sh
+export ASCEBD_RT_VISIBLE_DEVICES=0
+
+vllm serve ${MODEL_PATH} \
+          --host 0.0.0.0 \
+          --port 8000 \
+          --served-model-name qwen-2.5-7b-instruct \
+          --trust-remote-code \
+          --max-model-len 32768
+```
+
+### Multi-node Deployment
+
+Single-node deployment is recommended.
+
+### Prefill-Decode Disaggregation
+
+Not supported yet.
+
+## Functional Verification
+
+After starting the service, verify functionality using a `curl` request:
+
+```shell
+curl http://<IP>:<Port>/v1/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "qwen-2.5-7b-instruct",
+        "prompt": "Beijing is a",
+        "max_tokens": 5,
+        "temperature": 0
+    }'
+```
+
+A valid response (e.g., `"Beijing is a vibrant and historic capital city"`) indicates successful deployment.
+
+## Accuracy Evaluation
+
+### Using AISBench
+
+Refer to [Using AISBench](../developer_guide/evaluation/using_ais_bench.md) for details.
+
+Results and logs are saved to `benchmark/outputs/default/`. A sample accuracy report is shown below:
+
+| dataset | version | metric | mode | vllm-api-general-chat |
+|----- | ----- | ----- | ----- |--------------|
+| gsm8k | - | accuracy | gen | 75.00  |
+
+## 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 `Qwen2.5-7B-Instruct` 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
+vllm bench serve \
+  --model ./Qwen2.5-7B-Instruct/ \
+  --dataset-name random \
+  --random-input 200 \
+  --num-prompt 200 \
+  --request-rate 1 \
+  --save-result \
+  --result-dir ./perf_results/
+```
+
+After about several minutes, you can get the performance evaluation result.
diff --git a/docs/source/tutorials/index.md b/docs/source/tutorials/index.md
index a2151116..22767ff3 100644
--- a/docs/source/tutorials/index.md
+++ b/docs/source/tutorials/index.md
@@ -10,6 +10,7 @@ single_npu_qwen3_embedding
 single_npu_qwen3_quantization
 single_npu_qwen3_w4a4
 single_node_pd_disaggregation_mooncake
+Qwen2.5
 multi_npu_qwen3_next
 multi_npu
 multi_npu_kimi-k2-thinking