init v0.23.0

Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>
2026-08-27 15:11:51 +08:00
parent b582a8e7d1
commit 7f8a1b1f7a
2849 changed files with 712887 additions and 22001 deletions
--- a/docs/source/developer_guide/evaluation/index.md
+++ b/docs/source/developer_guide/evaluation/index.md
@@ -5,6 +5,6 @@
 :maxdepth: 1
 using_evalscope
 using_lm_eval
+using_ais_bench
 using_opencompass
-accuracy_report/index
 :::
--- a/docs/source/developer_guide/evaluation/using_ais_bench.md
+++ b/docs/source/developer_guide/evaluation/using_ais_bench.md
@@ -0,0 +1,333 @@
+# Using AISBench
+
+This document guides you to conduct accuracy testing using [AISBench](https://github.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
+   :substitutions:
+# Update DEVICE according to your device (/dev/davinci[0-7])
+export DEVICE=/dev/davinci7
+# Update the vllm-ascend image
+export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
+docker run --rm \
+--name vllm-ascend \
+--shm-size=1g \
+--device $DEVICE \
+--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 \
+-e VLLM_USE_MODELSCOPE=True \
+-e PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \
+-it $IMAGE \
+/bin/bash
+```
+
+Run the vLLM server in the docker.
+
+```{code-block} bash
+   :substitutions:
+vllm serve Qwen/Qwen2.5-0.5B-Instruct --max-model-len 35000 &
+```
+
+:::{note}
+`--max-model-len` should be greater than `35000`, this will be suitable for most datasets. Otherwise the accuracy evaluation may be affected.
+:::
+
+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.
+```
+
+### 2. Run different datasets using AISBench
+
+#### Install AISBench
+
+Refer to [AISBench](https://github.com/AISBench/benchmark/tree/master) for details.
+Install AISBench from source.
+
+```shell
+git clone https://github.com/AISBench/benchmark.git
+cd benchmark/
+pip3 install -e ./ --use-pep517
+```
+
+Install extra AISBench dependencies.
+
+```shell
+pip3 install -r requirements/api.txt
+pip3 install -r requirements/extra.txt
+```
+
+Run `ais_bench -h` to check the installation.
+
+#### Download Dataset
+
+You can choose one or multiple datasets to execute accuracy evaluation.
+
+1. `C-Eval` dataset.
+
+    Take `C-Eval` dataset as an example. You can refer to [Datasets](https://github.com/AISBench/benchmark/tree/master/ais_bench/benchmark/configs/datasets) for more datasets. Each dataset has a `README.md` with detailed download and installation instructions.
+
+    Download dataset and install it to specific path.
+
+    ```shell
+    cd ais_bench/datasets
+    mkdir ceval/
+    mkdir ceval/formal_ceval
+    cd ceval/formal_ceval
+    wget https://www.modelscope.cn/datasets/opencompass/ceval-exam/resolve/master/ceval-exam.zip
+    unzip ceval-exam.zip
+    rm ceval-exam.zip
+    ```
+
+2. `MMLU` dataset.
+
+    ```shell
+    cd ais_bench/datasets
+    wget http://opencompass.oss-cn-shanghai.aliyuncs.com/datasets/data/mmlu.zip
+    unzip mmlu.zip
+    rm mmlu.zip
+    ```
+
+3. `GPQA` dataset.
+
+    ```shell
+    cd ais_bench/datasets
+    wget http://opencompass.oss-cn-shanghai.aliyuncs.com/datasets/data/gpqa.zip
+    unzip gpqa.zip
+    rm gpqa.zip
+    ```
+
+4. `MATH` dataset.
+
+    ```shell
+    cd ais_bench/datasets
+    wget http://opencompass.oss-cn-shanghai.aliyuncs.com/datasets/data/math.zip
+    unzip math.zip
+    rm math.zip
+    ```
+
+5. `LiveCodeBench` dataset.
+
+    ```shell
+    cd ais_bench/datasets
+    git lfs install
+    git clone https://huggingface.co/datasets/livecodebench/code_generation_lite
+    ```
+
+6. `AIME 2024` dataset.
+
+    ```shell
+    cd ais_bench/datasets
+    mkdir aime/
+    cd aime/
+    wget http://opencompass.oss-cn-shanghai.aliyuncs.com/datasets/data/aime.zip
+    unzip aime.zip
+    rm aime.zip
+    ```
+
+7. `GSM8K` dataset.
+
+    ```shell
+    cd ais_bench/datasets
+    wget http://opencompass.oss-cn-shanghai.aliyuncs.com/datasets/data/gsm8k.zip
+    unzip gsm8k.zip
+    rm gsm8k.zip
+    ```
+
+#### Configuration
+
+Update the file `benchmark/ais_bench/benchmark/configs/models/vllm_api/vllm_api_general_chat.py`.
+There are several arguments that you should update according to your environment.
+
+- `attr`: Identifier for the inference backend type, fixed as `service` (serving-based inference) or `local` (local model).
+- `type`: Used to select different backend API types.
+- `abbr`: Unique identifier for a local task, used to distinguish between multiple tasks.
+- `path`: Update to your model weight path.
+- `model`: Update to your model name in vLLM.
+- `host_ip` and `host_port`: Update to your vLLM server ip and port.
+- `max_out_len`: Note `max_out_len` + LLM input length should be less than `max_model_len` (config in your vllm server), `32768` will be suitable for most datasets.
+- `batch_size`: Update according to your dataset.
+- `temperature`: Update inference argument.
+
+```python
+from ais_bench.benchmark.models import VLLMCustomAPIChat
+from ais_bench.benchmark.utils.model_postprocessors import extract_non_reasoning_content
+
+models = [
+    dict(
+        attr="service",
+        type=VLLMCustomAPIChat,
+        abbr='vllm-api-general-chat',
+        path="xxxx",
+        model="xxxx",
+        request_rate = 0,
+        retry = 2,
+        host_ip = "localhost",
+        host_port = 8000,
+        max_out_len = xxx,
+        batch_size = xxx,
+        trust_remote_code=False,
+        generation_kwargs = dict(
+            temperature = 0.6,
+            top_k = 10,
+            top_p = 0.95,
+            seed = None,
+            repetition_penalty = 1.03,
+        ),
+        pred_postprocessor=dict(type=extract_non_reasoning_content)
+    )
+]
+
+```
+
+#### Execute Accuracy Evaluation
+
+Run the following code to execute different accuracy evaluation.
+
+```shell
+# run C-Eval dataset
+ais_bench --models vllm_api_general_chat --datasets ceval_gen_0_shot_cot_chat_prompt.py --mode all --dump-eval-details --merge-ds
+
+# run MMLU dataset
+ais_bench --models vllm_api_general_chat --datasets mmlu_gen_0_shot_cot_chat_prompt.py --mode all --dump-eval-details --merge-ds
+
+# run GPQA dataset
+ais_bench --models vllm_api_general_chat --datasets gpqa_gen_0_shot_str.py --mode all --dump-eval-details --merge-ds
+
+# run MATH-500 dataset
+ais_bench --models vllm_api_general_chat --datasets math500_gen_0_shot_cot_chat_prompt.py --mode all --dump-eval-details --merge-ds
+
+# run LiveCodeBench dataset
+ais_bench --models vllm_api_general_chat --datasets livecodebench_code_generate_lite_gen_0_shot_chat.py --mode all --dump-eval-details --merge-ds
+
+# run AIME 2024 dataset
+ais_bench --models vllm_api_general_chat --datasets aime2024_gen_0_shot_chat_prompt.py --mode all --dump-eval-details --merge-ds
+
+# run GSM8K dataset
+ais_bench --models vllm_api_general_chat --datasets gsm8k_gen_0_shot_cot_chat_prompt.py --mode all --dump-eval-details --merge-ds
+
+```
+
+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
+├── logs # Execution logs; if --debug is added to the command, no intermediate logs are saved to disk (all are printed directly to the screen)
+│   ├── eval
+│   │   └── vllm-api-general-chat
+│   │       └── demo_gsm8k.out # Logs of the accuracy evaluation process based on inference results in the predictions/ folder
+│   └── infer
+│       └── vllm-api-general-chat
+│           └── demo_gsm8k.out # Logs of the inference process
+├── predictions
+│   └── vllm-api-general-chat
+│       └── demo_gsm8k.json # Inference results (all outputs returned by the inference service)
+├── results
+│   └── vllm-api-general-chat
+│       └── demo_gsm8k.json # Raw scores calculated from the accuracy evaluation
+└── summary
+    ├── summary_20250628_151326.csv # Final accuracy scores (in table format)
+    ├── summary_20250628_151326.md # Final accuracy scores (in Markdown format)
+    └── summary_20250628_151326.txt # Final accuracy scores (in text format)
+```
+
+#### Execute Performance Evaluation
+
+Text-only benchmarks:
+
+```shell
+# run C-Eval dataset
+ais_bench --models vllm_api_general_chat --datasets ceval_gen_0_shot_cot_chat_prompt.py --summarizer default_perf --mode perf
+
+# run MMLU dataset
+ais_bench --models vllm_api_general_chat --datasets mmlu_gen_0_shot_cot_chat_prompt.py --summarizer default_perf --mode perf
+
+# run GPQA dataset
+ais_bench --models vllm_api_general_chat --datasets gpqa_gen_0_shot_str.py --summarizer default_perf --mode perf
+
+# run MATH-500 dataset
+ais_bench --models vllm_api_general_chat --datasets math500_gen_0_shot_cot_chat_prompt.py --summarizer default_perf --mode perf
+
+# run LiveCodeBench dataset
+ais_bench --models vllm_api_general_chat --datasets livecodebench_code_generate_lite_gen_0_shot_chat.py --summarizer default_perf --mode perf
+
+# run AIME 2024 dataset
+ais_bench --models vllm_api_general_chat --datasets aime2024_gen_0_shot_chat_prompt.py --summarizer default_perf --mode perf
+
+# run GSM8K dataset
+ais_bench --models vllm_api_general_chat --datasets gsm8k_gen_0_shot_cot_str_perf.py --summarizer default_perf --mode perf
+```
+
+Multi-modal benchmarks (text + images):
+
+```shell
+# run textvqa dataset
+ais_bench --models vllm_api_stream_chat --datasets textvqa_gen_base64 --summarizer default_perf --mode perf
+```
+
+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
+|-- logs
+|   `-- performances
+|       `-- vllm-api-general-chat
+|           `-- cevaldataset.out # Logs of the performance evaluation process
+`-- performances
+    `-- vllm-api-general-chat
+        |-- cevaldataset.csv # Final performance results (in table format)
+        |-- cevaldataset.json # Final performance results (in json format)
+        |-- cevaldataset_details.h5 # Final performance results in details
+        |-- cevaldataset_details.json # Final performance results in details
+        |-- cevaldataset_plot.html # Final performance results (in html format)
+        `-- cevaldataset_rps_distribution_plot_with_actual_rps.html # Final performance results (in html format)
+```
+
+### 3. Troubleshooting
+
+#### Invalid Image Path Error
+
+If you download the TextVQA dataset following the AISBench documentation:
+
+```bash
+cd ais_bench/datasets
+git lfs install
+git clone https://huggingface.co/datasets/maoxx241/textvqa_subset
+mv textvqa_subset/ textvqa/
+mkdir textvqa/textvqa_json/
+mv textvqa/*.json textvqa/textvqa_json/
+mv textvqa/*.jsonl textvqa/textvqa_json/
+```
+
+you may encounter the following error:
+
+```bash
+AISBench - ERROR - /vllm-workspace/benchmark/ais_bench/benchmark/clients/base_client.py - raise_error - 35 - [AisBenchClientException] Request failed: HTTP status 400. Server response: {"error":{"message":"1 validation error for ChatCompletionContentPartImageParam\nimage_url\n  Input should be a valid dictionary [type=dict_type, input_value='data/textvqa/train_images/b2ae0f96dfbea5d8.jpg', input_type=str]\n    For further information visit https://errors.pydantic.dev/2.12/v/dict_type None","type":"BadRequestError","param":null,"code":400}}
+```
+
+You need to manually replace the dataset image paths with absolute paths, changing `/path/to/benchmark/ais_bench/datasets/textvqa/train_images/` to the actual absolute directory where the images are stored:
+
+```bash
+cd ais_bench/datasets/textvqa/textvqa_json
+sed -i 's#data/textvqa/train_images/#/path/to/benchmark/ais_bench/datasets/textvqa/train_images/#g' textvqa_val.json
+```
--- a/docs/source/developer_guide/evaluation/using_evalscope.md
+++ b/docs/source/developer_guide/evaluation/using_evalscope.md
@@ -1,8 +1,8 @@
 # Using EvalScope

-This document will guide you have model inference stress testing and accuracy testing using [EvalScope](https://github.com/modelscope/evalscope).
+This document will guide you through model inference stress testing and accuracy testing using [EvalScope](https://github.com/modelscope/evalscope).

-## 1. Online serving
+## 1. Online server

 You can run docker container to start the vLLM server on a single NPU:

@@ -13,6 +13,7 @@ export DEVICE=/dev/davinci7
 # Update the vllm-ascend image
 export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
 docker run --rm \
+--shm-size=1g \
 --name vllm-ascend \
 --device $DEVICE \
 --device /dev/davinci_manager \
@@ -31,30 +32,30 @@ docker run --rm \
 vllm serve Qwen/Qwen2.5-7B-Instruct --max_model_len 26240
 ```

-If your service start successfully, you can see the info shown below:
+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.
 ```

-Once your server is started, you can query the model with input prompts in new terminal:
+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 '{
        "model": "Qwen/Qwen2.5-7B-Instruct",
        "prompt": "The future of AI is",
-        "max_tokens": 7,
+        "max_completion_tokens": 7,
        "temperature": 0
    }'
 ```

 ## 2. Install EvalScope using pip

-You can install EvalScope by using:
+You can install EvalScope as follows:

 ```bash
 python3 -m venv .venv-evalscope
@@ -62,21 +63,21 @@ source .venv-evalscope/bin/activate
 pip install gradio plotly evalscope
 ```

-## 3. Run gsm8k accuracy test using EvalScope
+## 3. Run GSM8K using EvalScope for accuracy testing

-You can `evalscope eval` run gsm8k accuracy test:
+You can use `evalscope eval` to run GSM8K (a grade-school math benchmark dataset) for accuracy testing:

-```
+```shell
 evalscope eval \
 --model Qwen/Qwen2.5-7B-Instruct \
 --api-url http://localhost:8000/v1 \
 --api-key EMPTY \
- --eval-type service \
+ --eval-type server \
 --datasets gsm8k \
 --limit 10
 ```

-After 1-2 mins, the output is as shown below:
+After 1 to 2 minutes, the output is shown below:

 ```shell
 +---------------------+-----------+-----------------+----------+-------+---------+---------+
@@ -86,7 +87,7 @@ After 1-2 mins, the output is as shown below:
 +---------------------+-----------+-----------------+----------+-------+---------+---------+
 ```

-See more detail in: [EvalScope doc - Model API Service Evaluation](https://evalscope.readthedocs.io/en/latest/get_started/basic_usage.html#model-api-service-evaluation).
+See more details in [EvalScope doc - Model API Service Evaluation](https://evalscope.readthedocs.io/en/latest/get_started/basic_usage.html#model-api-service-evaluation).

 ## 4. Run model inference stress testing using EvalScope

@@ -98,9 +99,9 @@ pip install evalscope[perf] -U

 ### Basic usage

-You can use `evalscope perf` run perf test:
+You can use `evalscope perf` to run perf testing:

-```
+```shell
 evalscope perf \
    --url "http://localhost:8000/v1/chat/completions" \
    --parallel 5 \
@@ -113,7 +114,7 @@ evalscope perf \

 ### Output results

-After 1-2 mins, the output is as shown below:
+After 1 to 2 minutes, the output is shown below:

 ```shell
 Benchmarking summary:
@@ -172,4 +173,4 @@ Percentile results:
 +------------+----------+---------+-------------+--------------+---------------+----------------------+
 ```

-See more detail in: [EvalScope doc - Model Inference Stress Testing](https://evalscope.readthedocs.io/en/latest/user_guides/stress_test/quick_start.html#basic-usage).
+See more detail in [EvalScope doc - Model Inference Stress Testing](https://evalscope.readthedocs.io/en/latest/user_guides/stress_test/quick_start.html#basic-usage).
--- a/docs/source/developer_guide/evaluation/using_lm_eval.md
+++ b/docs/source/developer_guide/evaluation/using_lm_eval.md
@@ -1,9 +1,12 @@
 # Using lm-eval
-This document will guide you have a accuracy testing using [lm-eval][1].
+
+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:
+
+### 1. Start the vLLM server
+
+You can run a docker container to start the vLLM server on a single NPU:

 ```{code-block} bash
   :substitutions:
@@ -13,6 +16,7 @@ export DEVICE=/dev/davinci7
 export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
 docker run --rm \
 --name vllm-ascend \
+--shm-size=1g \
 --device $DEVICE \
 --device /dev/davinci_manager \
 --device /dev/devmm_svm \
@@ -31,46 +35,54 @@ docker run --rm \
 vllm serve Qwen/Qwen2.5-0.5B-Instruct --max_model_len 4096 &
 ```

-Started the vLLM server successfully,if you see log as below:
+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.
 ```

-### 2. Run gsm8k accuracy test using lm-eval
+### 2. Run GSM8K using the vLLM server (curl) and then run lm-eval for accuracy testing

-You can query result with input prompts:
+You can query the result with input prompts:
+
+```shell
+PROMPT='<|im_start|>system
+You are a professional accountant. Answer questions using accounting knowledge, output only the option letter (A/B/C/D).<|im_end|>
+<|im_start|>user
+Question: A company'"'"'s balance sheet as of December 31, 2023 shows:
+  Current assets: Cash and equivalents 5 million yuan, Accounts receivable 8 million yuan, Inventory 6 million yuan
+  Non-current assets: Net fixed assets 12 million yuan
+  Current liabilities: Short-term loans 4 million yuan, Accounts payable 3 million yuan
+  Non-current liabilities: Long-term loans 9 million yuan
+  Owner'"'"'s equity: Paid-in capital 10 million yuan, Retained earnings ?
+Requirement: Calculate the company'"'"'s Asset-Liability Ratio and Current Ratio (round to two decimal places).
+Options:
+A. Asset-Liability Ratio=58.33%, Current Ratio=1.90
+B. Asset-Liability Ratio=62.50%, Current Ratio=2.17
+C. Asset-Liability Ratio=65.22%, Current Ratio=1.75
+D. Asset-Liability Ratio=68.00%, Current Ratio=2.50<|im_end|>
+<|im_start|>assistant
+'

-```
 curl http://localhost:8000/v1/completions \
    -H "Content-Type: application/json" \
-    -d '{
-        "model": "Qwen/Qwen2.5-0.5B-Instruct",
-        "prompt": "'"<|im_start|>system\nYou are a professional accountant. Answer questions using accounting knowledge, output only the option letter (A/B/C/D).<|im_end|>\n"\
-"<|im_start|>user\nQuestion: A company's balance sheet as of December 31, 2023 shows:\n"\
-"  Current assets: Cash and equivalents 5 million yuan, Accounts receivable 8 million yuan, Inventory 6 million yuan\n"\
-"  Non-current assets: Net fixed assets 12 million yuan\n"\
-"  Current liabilities: Short-term loans 4 million yuan, Accounts payable 3 million yuan\n"\
-"  Non-current liabilities: Long-term loans 9 million yuan\n"\
-"  Owner's equity: Paid-in capital 10 million yuan, Retained earnings ?\n"\
-"Requirement: Calculate the company's Asset-Liability Ratio and Current Ratio (round to two decimal places).\n"\
-"Options:\n"\
-"A. Asset-Liability Ratio=58.33%, Current Ratio=1.90\n"\
-"B. Asset-Liability Ratio=62.50%, Current Ratio=2.17\n"\
-"C. Asset-Liability Ratio=65.22%, Current Ratio=1.75\n"\
-"D. Asset-Liability Ratio=68.00%, Current Ratio=2.50<|im_end|>\n"\
-"<|im_start|>assistant\n"'",
-        "max_tokens": 1,
-        "temperature": 0,
-        "stop": ["<|im_end|>"]
-    }' | python3 -m json.tool
+    -d "$(jq -n \
+        --arg model "Qwen/Qwen2.5-0.5B-Instruct" \
+        --arg prompt "$PROMPT" \
+        '{
+            model: $model,
+            prompt: $prompt,
+            max_completion_tokens: 1,
+            temperature: 0,
+            stop: ["<|im_end|>"]
+        }')" | python3 -m json.tool
 ```

 The output format matches the following:

-```
+```json
 {
    "id": "cmpl-2f678e8bdf5a4b209a3f2c1fa5832e25",
    "object": "text_completion",
@@ -98,16 +110,24 @@ The output format matches the following:
 }
 ```

-Install lm-eval in the container.
+Install lm-eval in the container:

 ```bash
 export HF_ENDPOINT="https://hf-mirror.com"
+export USE_MODELSCOPE_HUB=0
 pip install lm-eval[api]
 ```

+:::{note}
+The Docker container is launched with `VLLM_USE_MODELSCOPE=True`, which may
+cause lm-eval to download datasets from ModelScope instead of HuggingFace.
+Setting `USE_MODELSCOPE_HUB=0` disables this behavior so that lm-eval can
+fetch datasets from HuggingFace correctly.
+:::
+
 Run the following command:

-```
+```shell
 # Only test gsm8k dataset in this demo
 lm_eval \
  --model local-completions \
@@ -116,19 +136,20 @@ lm_eval \
  --output_path ./
 ```

-After 30 mins, the output is as shown below:
+After 30 minutes, the output is as shown below:

-```
-The markdown format results is as below:
+```shell
+The results in Markdown format are as follows:

-Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
+|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
 |-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
 |gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.3215|±  |0.0129|
-|     |       |strict-match    |     5|exact_match|↑  |0.2077|±  |0.0112|
+|gsm8k|      3|strict-match    |     5|exact_match|↑  |0.2077|±  |0.0112|

 ```

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

 You can run docker container on a single NPU:
@@ -141,6 +162,7 @@ export DEVICE=/dev/davinci7
 export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
 docker run --rm \
 --name vllm-ascend \
+--shm-size=1g \
 --device $DEVICE \
 --device /dev/davinci_manager \
 --device /dev/devmm_svm \
@@ -158,17 +180,26 @@ docker run --rm \
 /bin/bash
 ```

-### 2. Run gsm8k accuracy test using lm-eval
-Install lm-eval in the container.
+### 2. Run GSM8K using lm-eval for accuracy testing
+
+Install lm-eval in the container:

 ```bash
 export HF_ENDPOINT="https://hf-mirror.com"
+export USE_MODELSCOPE_HUB=0
 pip install lm-eval
 ```

+:::{note}
+The Docker container is launched with `VLLM_USE_MODELSCOPE=True`, which may
+cause lm-eval to download datasets from ModelScope instead of HuggingFace.
+Setting `USE_MODELSCOPE_HUB=0` disables this behavior so that lm-eval can
+fetch datasets from HuggingFace correctly.
+:::
+
 Run the following command:

-```
+```shell
 # Only test gsm8k dataset in this demo
 lm_eval \
  --model vllm \
@@ -177,21 +208,21 @@ lm_eval \
  --batch_size auto
 ```

-After 1-2 mins, the output is as shown below:
+After 1 to 2 minutes, the output is shown below:

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

-Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
+|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
 |-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
 |gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.3412|±  |0.0131|
-|     |       |strict-match    |     5|exact_match|↑  |0.3139|±  |0.0128|
+|gsm8k|      3|strict-match    |     5|exact_match|↑  |0.3139|±  |0.0128|

 ```

-## Use offline Datasets
+## Use Offline Datasets

-Take gsm8k(single dataset) and mmlu(multi-subject dataset) as examples, and you can see more from [here][2].
+Take GSM8K (single dataset) and MMLU (multi-subject dataset) as examples, and you can see more from [using-local-datasets][2].

 ```bash
 # set HF_DATASETS_OFFLINE when using offline datasets
@@ -205,7 +236,7 @@ cd lm_eval/tasks/gsm8k
 cd lm_eval/tasks/mmlu/default
 ```

-set [gsm8k.yaml][3] as follows:
+Set [gsm8k.yaml][3] as follows:

 ```yaml
 tag:
@@ -230,7 +261,7 @@ training_split: train
 fewshot_split: train
 test_split: test
 doc_to_text: 'Q: {{question}}
-  A(Please follow the summarize the result at the end with the format of "The answer is xxx", where xx is the result.):'
+  A(Please follow the summarized result at the end with the format of "The answer is xxx", where xx is the result.):'
 doc_to_target: "{{answer}}" #" {{answer.split('### ')[-1].rstrip()}}"
 metric_list:
  - metric: exact_match
@@ -268,7 +299,7 @@ metadata:
  version: 3.0
 ```

-set [_default_template_yaml][4] as follows:
+Set [_default_template_yaml][4] as follows:

 ```yaml
 # set dataset_path according to the downloaded dataset
--- a/docs/source/developer_guide/evaluation/using_opencompass.md
+++ b/docs/source/developer_guide/evaluation/using_opencompass.md
@@ -1,9 +1,10 @@
 # Using OpenCompass
-This document will guide you have a accuracy testing using [OpenCompass](https://github.com/open-compass/opencompass).

-## 1. Online Serving
+This document guides you to conduct accuracy testing using [OpenCompass](https://github.com/open-compass/opencompass).

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

 ```{code-block} bash
   :substitutions:
@@ -13,6 +14,7 @@ export DEVICE=/dev/davinci7
 export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
 docker run --rm \
 --name vllm-ascend \
+--shm-size=1g \
 --device $DEVICE \
 --device /dev/davinci_manager \
 --device /dev/devmm_svm \
@@ -30,29 +32,30 @@ docker run --rm \
 vllm serve Qwen/Qwen2.5-7B-Instruct --max_model_len 26240
 ```

-If your service start successfully, you can see the info shown below:
+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.
 ```

-Once your server is started, you can query the model with input prompts in new terminal:
+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 '{
        "model": "Qwen/Qwen2.5-7B-Instruct",
        "prompt": "The future of AI is",
-        "max_tokens": 7,
+        "max_completion_tokens": 7,
        "temperature": 0
    }'
 ```

-## 2. Run ceval accuracy test using OpenCompass
-Install OpenCompass and configure the environment variables in the container.
+## 2. Run C-Eval (a Chinese language model evaluation benchmark) using OpenCompass for accuracy testing
+
+Install OpenCompass and configure the environment variables in the container:

 ```bash
 # Pin Python 3.10 due to:
@@ -64,7 +67,7 @@ export DATASET_SOURCE=ModelScope
 git clone https://github.com/open-compass/opencompass.git
 ```

-Add `opencompass/configs/eval_vllm_ascend_demo.py` with the following content:
+Add the following content to `opencompass/configs/eval_vllm_ascend_demo.py`:

 ```python
 from mmengine.config import read_base
@@ -106,14 +109,14 @@ models = [

 Run the following command:

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

-After 1-2 mins, the output is as shown below:
+After 1 to 2 minutes, the output is shown below:

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

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