[Lint]Style: reformat markdown files via markdownlint (#5884)

### What this PR does / why we need it? reformat markdown files via markdownlint - vLLM version: v0.13.0 - vLLM main: bde38c11df --------- Signed-off-by: root <root@LAPTOP-VQKDDVMG.localdomain> Signed-off-by: MrZ20 <2609716663@qq.com> Co-authored-by: root <root@LAPTOP-VQKDDVMG.localdomain>
2026-01-15 09:06:01 +08:00
parent 96edd4673f
commit 4811ba62e0
75 changed files with 711 additions and 308 deletions
--- a/docs/source/developer_guide/evaluation/using_ais_bench.md
+++ b/docs/source/developer_guide/evaluation/using_ais_bench.md
@@ -1,8 +1,11 @@
 # Using AISBench
+
 This document guides you to conduct accuracy testing using [AISBench](https://gitee.com/aisbench/benchmark/tree/master). AISBench provides accuracy and performance evaluation for many datasets.

 ## Online Server
+
 ### 1. Start the vLLM server
+
 You can run docker container to start the vLLM server on a single NPU:

 ```{code-block} bash
@@ -44,7 +47,7 @@ vllm serve Qwen/Qwen2.5-0.5B-Instruct --max_model_len 35000 &

 The vLLM server is started successfully, if you see logs as below:

-```
+```shell
 INFO:     Started server process [9446]
 INFO:     Waiting for application startup.
 INFO:     Application startup complete.
@@ -220,7 +223,7 @@ ais_bench --models vllm_api_general_chat --datasets aime2024_gen_0_shot_chat_pro

 After each dataset execution, you can get the result from saved files such as `outputs/default/20250628_151326`, there is an example as follows:

-```
+```shell
 20250628_151326/
 ├── configs # Combined configuration file for model tasks, dataset tasks, and result presentation tasks
 │   └── 20250628_151326_29317.py
@@ -276,7 +279,7 @@ ais_bench --models vllm_api_stream_chat --datasets textvqa_gen_base64 --summariz

 After execution, you can get the result from saved files, there is an example as follows:

-```
+```shell
 20251031_070226/
 |-- configs # Combined configuration file for model tasks, dataset tasks, and result presentation tasks
 |   `-- 20251031_070226_122485.py
--- a/docs/source/developer_guide/evaluation/using_evalscope.md
+++ b/docs/source/developer_guide/evaluation/using_evalscope.md
@@ -34,7 +34,7 @@ vllm serve Qwen/Qwen2.5-7B-Instruct --max_model_len 26240

 If the vLLM server is started successfully, you can see information shown below:

-```
+```shell
 INFO:     Started server process [6873]
 INFO:     Waiting for application startup.
 INFO:     Application startup complete.
@@ -42,7 +42,7 @@ INFO:     Application startup complete.

 Once your server is started, you can query the model with input prompts in a new terminal:

-```
+```shell
 curl http://localhost:8000/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
@@ -67,7 +67,7 @@ pip install gradio plotly evalscope

 You can use `evalscope eval` to run GSM8K for accuracy testing:

-```
+```shell
 evalscope eval \
 --model Qwen/Qwen2.5-7B-Instruct \
 --api-url http://localhost:8000/v1 \
@@ -101,7 +101,7 @@ pip install evalscope[perf] -U

 You can use `evalscope perf` to run perf testing:

-```
+```shell
 evalscope perf \
    --url "http://localhost:8000/v1/chat/completions" \
    --parallel 5 \
--- a/docs/source/developer_guide/evaluation/using_lm_eval.md
+++ b/docs/source/developer_guide/evaluation/using_lm_eval.md
@@ -1,8 +1,11 @@
 # Using lm-eval
+
 This document guides you to conduct accuracy testing using [lm-eval][1].

 ## Online Server
+
 ### 1. Start the vLLM server
+
 You can run docker container to start the vLLM server on a single NPU:

 ```{code-block} bash
@@ -34,7 +37,7 @@ vllm serve Qwen/Qwen2.5-0.5B-Instruct --max_model_len 4096 &

 The vLLM server is started successfully, if you see logs as below:

-```
+```shell
 INFO:     Started server process [9446]
 INFO:     Waiting for application startup.
 INFO:     Application startup complete.
@@ -44,7 +47,7 @@ INFO:     Application startup complete.

 You can query the result with input prompts:

-```
+```shell
 curl http://localhost:8000/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
@@ -71,7 +74,7 @@ curl http://localhost:8000/v1/completions \

 The output format matches the following:

-```
+```json
 {
    "id": "cmpl-2f678e8bdf5a4b209a3f2c1fa5832e25",
    "object": "text_completion",
@@ -108,7 +111,7 @@ pip install lm-eval[api]

 Run the following command:

-```
+```shell
 # Only test gsm8k dataset in this demo
 lm_eval \
  --model local-completions \
@@ -119,7 +122,7 @@ lm_eval \

 After 30 minutes, the output is as shown below:

-```
+```shell
 The markdown format results is as below:

 |Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
@@ -130,6 +133,7 @@ The markdown format results is as below:
 ```

 ## Offline Server
+
 ### 1. Run docker container

 You can run docker container on a single NPU:
@@ -161,6 +165,7 @@ docker run --rm \
 ```

 ### 2. Run GSM8K using lm-eval for accuracy testing
+
 Install lm-eval in the container:

 ```bash
@@ -170,7 +175,7 @@ pip install lm-eval

 Run the following command:

-```
+```shell
 # Only test gsm8k dataset in this demo
 lm_eval \
  --model vllm \
@@ -181,7 +186,7 @@ lm_eval \

 After 1 to 2 minutes, the output is shown below:

-```
+```shell
 The markdown format results is as below:

 Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
--- a/docs/source/developer_guide/evaluation/using_opencompass.md
+++ b/docs/source/developer_guide/evaluation/using_opencompass.md
@@ -1,4 +1,5 @@
 # Using OpenCompass
+
 This document guides you to conduct accuracy testing using [OpenCompass](https://github.com/open-compass/opencompass).

 ## 1. Online Server
@@ -33,7 +34,7 @@ vllm serve Qwen/Qwen2.5-7B-Instruct --max_model_len 26240

 The vLLM server is started successfully, if you see information as below:

-```
+```shell
 INFO:     Started server process [6873]
 INFO:     Waiting for application startup.
 INFO:     Application startup complete.
@@ -41,7 +42,7 @@ INFO:     Application startup complete.

 Once your server is started, you can query the model with input prompts in a new terminal.

-```
+```shell
 curl http://localhost:8000/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
@@ -53,6 +54,7 @@ curl http://localhost:8000/v1/completions \
 ```

 ## 2. Run C-Eval using OpenCompass for accuracy testing
+
 Install OpenCompass and configure the environment variables in the container:

 ```bash
@@ -107,13 +109,13 @@ models = [

 Run the following command:

-```
+```shell
 python3 run.py opencompass/configs/eval_vllm_ascend_demo.py --debug
 ```

 After 1 to 2 minutes, the output is shown below:

-```
+```shell
 The markdown format results is as below:

 | dataset | version | metric | mode | Qwen2.5-7B-Instruct-vLLM-API |