[1/2/N] Enable pymarkdown and python __init__ for lint system (#2011)

### What this PR does / why we need it?
1. Enable pymarkdown check
2. Enable python `__init__.py` check for vllm and vllm-ascend
3. Make clean code

### How was this patch tested?


- vLLM version: v0.9.2
- vLLM main:
29c6fbe58c

---------

Signed-off-by: wangli <wangli858794774@gmail.com>

docs/source/community/governance.md

@@ -1,7 +1,7 @@
 # Governance
 
 ## Mission
-As a vital component of vLLM, the vLLM Ascend project is dedicated to providing an easy, fast, and cheap LLM Serving for Everyone on Ascend NPU, and to actively contribute to the enrichment of vLLM. 
+As a vital component of vLLM, the vLLM Ascend project is dedicated to providing an easy, fast, and cheap LLM Serving for Everyone on Ascend NPU, and to actively contribute to the enrichment of vLLM.
 
 ## Principles
 vLLM Ascend follows the vLLM community's code of conduct：[vLLM - CODE OF CONDUCT](https://github.com/vllm-project/vllm/blob/main/CODE_OF_CONDUCT.md)
@@ -13,7 +13,7 @@ vLLM Ascend is an open-source project under the vLLM community, where the author
 
     **Responsibility:** Help new contributors on boarding, handle and respond to community questions, review RFCs, code
 
-    **Requirements:** Complete at least 1 contribution. Contributor is someone who consistently and actively participates in a project, included but not limited to issue/review/commits/community involvement. 
+    **Requirements:** Complete at least 1 contribution. Contributor is someone who consistently and actively participates in a project, included but not limited to issue/review/commits/community involvement.
 
     Contributors will be empowered [vllm-project/vllm-ascend](https://github.com/vllm-project/vllm-ascend) Github repo `Triage` permissions (`Can read and clone this repository. Can also manage issues and pull requests`) to help community developers collaborate more efficiently.
 

docs/source/community/user_stories/llamafactory.md

@@ -4,7 +4,7 @@
 
 [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) is an easy-to-use and efficient platform for training and fine-tuning large language models. With LLaMA-Factory, you can fine-tune hundreds of pre-trained models locally without writing any code.
 
-LLaMA-Facotory users need to evaluate and inference the model after fine-tuning the model. 
+LLaMA-Facotory users need to evaluate and inference the model after fine-tuning the model.
 
 **The Business Challenge**
 

docs/source/developer_guide/contribution/index.md

@@ -13,6 +13,7 @@ But you can still set up dev env on Linux/Windows/macOS for linting and basic
 test as following commands:
 
 #### Run lint locally
+
 ```bash
 # Choose a base dir (~/vllm-project/) and set up venv
 cd ~/vllm-project/
@@ -103,7 +104,6 @@ If the PR spans more than one category, please include all relevant prefixes.
 You may find more information about contributing to vLLM Ascend backend plugin on [<u>docs.vllm.ai</u>](https://docs.vllm.ai/en/latest/contributing/overview.html).
 If you find any problem when contributing, you can feel free to submit a PR to improve the doc to help other developers.
 
-
 :::{toctree}
 :caption: Index
 :maxdepth: 1

docs/source/developer_guide/contribution/testing.md

@@ -172,6 +172,7 @@ pytest -sv tests/ut
 # Run single test
 pytest -sv tests/ut/test_ascend_config.py
 ```
+
 ::::
 
 ::::{tab-item} Multi cards test
@@ -185,6 +186,7 @@ pytest -sv tests/ut
 # Run single test
 pytest -sv tests/ut/test_ascend_config.py
 ```
+
 ::::
 
 :::::
@@ -218,10 +220,12 @@ VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/singlecard/test_offline_inference.
 # Run a certain case in test script
 VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/singlecard/test_offline_inference.py::test_models
 ```
+
 ::::
 
 ::::{tab-item} Multi cards test
 :sync: multi
+
 ```bash
 cd /vllm-workspace/vllm-ascend/
 # Run all single card the tests
@@ -233,6 +237,7 @@ VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/multicard/test_dynamic_npugraph_ba
 # Run a certain case in test script
 VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/multicard/test_offline_inference.py::test_models
 ```
+
 ::::
 
 :::::

docs/source/developer_guide/evaluation/accuracy_report/index.md

@@ -3,4 +3,4 @@
 :::{toctree}
 :caption: Accuracy Report
 :maxdepth: 1
-:::
+:::

docs/source/developer_guide/evaluation/using_evalscope.md

@@ -65,6 +65,7 @@ pip install gradio plotly evalscope
 ## 3. Run gsm8k accuracy test using EvalScope
 
 You can `evalscope eval` run gsm8k accuracy test:
+
 ```
 evalscope eval \
  --model Qwen/Qwen2.5-7B-Instruct \
@@ -98,6 +99,7 @@ pip install evalscope[perf] -U
 ### Basic usage
 
 You can use `evalscope perf` run perf test:
+
 ```
 evalscope perf \
     --url "http://localhost:8000/v1/chat/completions" \
@@ -111,7 +113,7 @@ evalscope perf \
 
 ### Output results
 
-After 1-2 mins, the output is as shown below: 
+After 1-2 mins, the output is as shown below:
 
 ```shell
 Benchmarking summary:

docs/source/developer_guide/evaluation/using_lm_eval.md

@@ -1,7 +1,7 @@
 # Using lm-eval
 This document will guide you have a accuracy testing using [lm-eval](https://github.com/EleutherAI/lm-evaluation-harness).
 
-##  1. Run docker container
+## 1. Run docker container
 
 You can run docker container on a single NPU:
 
@@ -36,6 +36,7 @@ Install lm-eval in the container.
 ```bash
 pip install lm-eval
 ```
+
 Run the following command:
 
 ```

docs/source/developer_guide/evaluation/using_opencompass.md

@@ -1,4 +1,4 @@
-# Using OpenCompass 
+# Using OpenCompass
 This document will guide you have a accuracy testing using [OpenCompass](https://github.com/open-compass/opencompass).
 
 ## 1. Online Serving
@@ -29,7 +29,9 @@ docker run --rm \
 -it $IMAGE \
 vllm serve Qwen/Qwen2.5-7B-Instruct --max_model_len 26240
 ```
+
 If your service start successfully, you can see the info shown below:
+
 ```
 INFO:     Started server process [6873]
 INFO:     Waiting for application startup.
@@ -37,6 +39,7 @@ INFO:     Application startup complete.
 ```
 
 Once your server is started, you can query the model with input prompts in new terminal:
+
 ```
 curl http://localhost:8000/v1/completions \
     -H "Content-Type: application/json" \

docs/source/developer_guide/feature_guide/patch.md

@@ -50,6 +50,7 @@ Before writing a patch, following the principle above, we should patch the least
 2. Decide which process we should patch. For example, here `distributed` belongs to the vLLM main process, so we should patch `platform`.
 3. Create the patch file in the right folder. The file should be named as `patch_{module_name}.py`. The example here is `vllm_ascend/patch/platform/patch_common/patch_distributed.py`.
 4. Write your patch code in the new file. Here is an example:
+
     ```python
     import vllm
 
@@ -59,8 +60,10 @@ Before writing a patch, following the principle above, we should patch the least
 
     vllm.distributed.parallel_state.destroy_model_parallel = patch_destroy_model_parallel
     ```
+
 5. Import the patch file in `__init__.py`. In this example, add `import vllm_ascend.patch.platform.patch_common.patch_distributed` into `vllm_ascend/patch/platform/patch_common/__init__.py`.
 6. Add the description of the patch in `vllm_ascend/patch/__init__.py`. The description format is as follows:
+
     ```
     # ** File: <The patch file name> **
     # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -74,8 +77,8 @@ Before writing a patch, following the principle above, we should patch the least
     #    Future Plan:
     #       <Describe the future plan to remove the patch>
     ```
-7. Add the Unit Test and E2E Test. Any newly added code in vLLM Ascend should contain the Unit Test and E2E Test as well. You can find more details in [test guide](../contribution/testing.md)
 
+7. Add the Unit Test and E2E Test. Any newly added code in vLLM Ascend should contain the Unit Test and E2E Test as well. You can find more details in [test guide](../contribution/testing.md)
 
 ## Limitation
 1. In V1 Engine, vLLM starts three kinds of process: Main process, EngineCore process and Worker process. Now vLLM Ascend only support patch the code in Main process and Worker process by default. If you want to patch the code runs in EngineCore process, you should patch EngineCore process entirely during setup, the entry code is here `vllm.v1.engine.core`. Please override `EngineCoreProc` and `DPEngineCoreProc` entirely.

docs/source/developer_guide/modeling/adding_a_new_model.md

@@ -216,6 +216,7 @@ The first argument of `vllm.ModelRegistry.register_model()` indicates the unique
   ],
 }
 ```
+
 :::
 
 ## Step 3: Verification

docs/source/developer_guide/performance/performance_benchmark.md

@@ -4,6 +4,7 @@ This document details the benchmark methodology for vllm-ascend, aimed at evalua
 **Benchmark Coverage**: We measure offline e2e latency and throughput, and fixed-QPS online serving benchmarks, for more details see [vllm-ascend benchmark scripts](https://github.com/vllm-project/vllm-ascend/tree/main/benchmarks).
 
 ## 1. Run docker container
+
 ```{code-block} bash
    :substitutions:
 # Update DEVICE according to your device (/dev/davinci[0-7])
@@ -29,6 +30,7 @@ docker run --rm \
 ```
 
 ## 2. Install dependencies
+
 ```bash
 cd /workspace/vllm-ascend
 pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
@@ -37,11 +39,13 @@ pip install -r benchmarks/requirements-bench.txt
 
 ## 3. (Optional)Prepare model weights
 For faster running speed, we recommend downloading the model in advance：
+
 ```bash
 modelscope download --model LLM-Research/Meta-Llama-3.1-8B-Instruct
 ```
 
 You can also replace all model paths in the [json](https://github.com/vllm-project/vllm-ascend/tree/main/benchmarks/tests) files with your local paths:
+
 ```bash
 [
   {
@@ -59,11 +63,13 @@ You can also replace all model paths in the [json](https://github.com/vllm-proje
 
 ## 4. Run benchmark script
 Run benchmark script:
+
 ```bash
 bash benchmarks/scripts/run-performance-benchmarks.sh
 ```
 
 After about 10 mins, the output is as shown below:
+
 ```bash
 online serving:
 qps 1:
@@ -173,6 +179,7 @@ Throughput: 4.64 requests/s, 2000.51 total tokens/s, 1010.54 output tokens/s
 Total num prompt tokens:  42659
 Total num output tokens:  43545
 ```
+
 The result json files are generated into the path `benchmark/results`
 These files contain detailed benchmarking results for further analysis.
 

docs/source/developer_guide/performance/profile_execute_duration.md

@@ -10,6 +10,7 @@ The execution duration of each stage (including pre/post-processing, model forwa
 * Use the blocking API `ProfileExecuteDuration().pop_captured_sync` at an appropriate time to get and print the execution durations of all observed stages.
 
 **We have instrumented the key inference stages (including pre-processing, model forward pass, etc.) for execute duration profiling. Execute the script as follows:**
+
 ```
 VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE=1 python3 vllm-ascend/examples/offline_inference_npu.py
 ```
@@ -36,4 +37,4 @@ VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE=1 python3 vllm-ascend/examples/offline_in
 5747:(IntegratedWorker pid=1502524) Profile execute duration [Decode]: [post process]:14.21ms [prepare input and forward]:10.10ms [forward]:4.52ms
 5751:(IntegratedWorker pid=1502524) Profile execute duration [Decode]: [post process]:15.03ms [prepare input and forward]:10.00ms [forward]:4.42ms
 
-```
+```

docs/source/installation.md

@@ -190,6 +190,7 @@ git clone https://github.com/vllm-project/vllm-ascend.git
 cd vllm-ascend
 docker build -t vllm-ascend-dev-image:latest -f ./Dockerfile .
 ```
+
 :::
 
 ```{code-block} bash

docs/source/quick_start.md

@@ -35,6 +35,7 @@ docker run --rm \
 # Install curl
 apt-get update -y && apt-get install -y curl
 ```
+
 ::::
 
 ::::{tab-item} openEuler
@@ -63,6 +64,7 @@ docker run --rm \
 # Install curl
 yum update -y && yum install -y curl
 ```
+
 ::::
 :::::
 
@@ -73,6 +75,7 @@ The default workdir is `/workspace`, vLLM and vLLM Ascend code are placed in `/v
 You can use Modelscope mirror to speed up download:
 
 <!-- tests/e2e/doctest/001-quickstart-test.sh should be considered updating as well -->
+
 ```bash
 export VLLM_USE_MODELSCOPE=true
 ```
@@ -87,6 +90,7 @@ With vLLM installed, you can start generating texts for list of input prompts (i
 Try to run below Python script directly or use `python3` shell to generate texts:
 
 <!-- tests/e2e/doctest/001-quickstart-test.sh should be considered updating as well -->
+
 ```python
 from vllm import LLM, SamplingParams
 
@@ -115,6 +119,7 @@ the following command to start the vLLM server with the
 [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) model:
 
 <!-- tests/e2e/doctest/001-quickstart-test.sh should be considered updating as well -->
+
 ```bash
 # Deploy vLLM server (The first run will take about 3-5 mins (10 MB/s) to download models)
 vllm serve Qwen/Qwen2.5-0.5B-Instruct &
@@ -128,11 +133,13 @@ INFO:     Waiting for application startup.
 INFO:     Application startup complete.
 INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
 ```
+
 Congratulations, you have successfully started the vLLM server!
 
 You can query the list the models:
 
 <!-- tests/e2e/doctest/001-quickstart-test.sh should be considered updating as well -->
+
 ```bash
 curl http://localhost:8000/v1/models | python3 -m json.tool
 ```
@@ -140,6 +147,7 @@ curl http://localhost:8000/v1/models | python3 -m json.tool
 You can also query the model with input prompts:
 
 <!-- tests/e2e/doctest/001-quickstart-test.sh should be considered updating as well -->
+
 ```bash
 curl http://localhost:8000/v1/completions \
     -H "Content-Type: application/json" \
@@ -155,12 +163,14 @@ vLLM is serving as background process, you can use `kill -2 $VLLM_PID` to stop t
 it's equal to `Ctrl-C` to stop foreground vLLM process:
 
 <!-- tests/e2e/doctest/001-quickstart-test.sh should be considered updating as well -->
+
 ```bash
   VLLM_PID=$(pgrep -f "vllm serve")
   kill -2 "$VLLM_PID"
 ```
 
 You will see output as below:
+
 ```
 INFO:     Shutting down FastAPI HTTP server.
 INFO:     Shutting down
@@ -170,4 +180,4 @@ INFO:     Application shutdown complete.
 
 Finally, you can exit container by using `ctrl-D`.
 ::::
-:::::
+:::::

docs/source/tutorials/multi_node.md

@@ -43,11 +43,13 @@ Execute the following commands on each node in sequence. The results must all be
 
 ### NPU Interconnect Verification:
 #### 1. Get NPU IP Addresses
+
 ```bash
 for i in {0..7}; do hccn_tool -i $i -ip -g | grep ipaddr; done
 ```
 
 #### 2. Cross-Node PING Test
+
 ```bash
 # Execute on the target node (replace with actual IP)
 hccn_tool -i 0 -ping -g address 10.20.0.20
@@ -95,6 +97,7 @@ Before launch the inference server, ensure some environment variables are set fo
 Run the following scripts on two nodes respectively
 
 **node0**
+
 ```shell
 #!/bin/sh
 
@@ -135,6 +138,7 @@ vllm serve /root/.cache/ds_v3 \
 ```
 
 **node1**
+
 ```shell
 #!/bin/sh
 
@@ -173,7 +177,7 @@ vllm serve /root/.cache/ds_v3 \
 --additional-config '{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
 ```
 
-The Deployment view looks like: 
+The Deployment view looks like:
 ![alt text](../assets/multi_node_dp.png)
 
 Once your server is started, you can query the model with input prompts:
@@ -191,6 +195,7 @@ curl http://{ node0 ip:8004 }/v1/completions \
 
 ## Run benchmarks
 For details please refer to [benchmark](https://github.com/vllm-project/vllm-ascend/tree/main/benchmarks)
+
 ```shell
 vllm bench serve --model /root/.cache/ds_v3  --served-model-name deepseek_v3 \
 --dataset-name random --random-input-len 128 --random-output-len 128 \

docs/source/tutorials/multi_npu_moge.md

@@ -71,6 +71,7 @@ curl http://localhost:8000/v1/completions \
     "temperature": 0.6
   }'
 ```
+
 ::::
 
 ::::{tab-item} v1/chat/completions
@@ -91,6 +92,7 @@ curl http://localhost:8000/v1/chat/completions \
         "add_special_tokens" : true
     }'
 ```
+
 ::::
 :::::
 
@@ -170,9 +172,11 @@ if __name__ == "__main__":
     del llm
     clean_up()
 ```
+
 ::::
 
 ::::{tab-item} Eager Mode
+
 ```{code-block} python
    :substitutions:
 import gc
@@ -226,6 +230,7 @@ if __name__ == "__main__":
     del llm
     clean_up()
 ```
+
 ::::
 :::::
 

docs/source/tutorials/multi_npu_quantization.md

@@ -30,7 +30,7 @@ docker run --rm \
 
 ## Install modelslim and convert model
 :::{note}
-You can choose to convert the model yourself or use the quantized model we uploaded, 
+You can choose to convert the model yourself or use the quantized model we uploaded,
 see https://www.modelscope.cn/models/vllm-ascend/QwQ-32B-W8A8
 :::
 
@@ -55,6 +55,7 @@ python3 quant_qwen.py --model_path $MODEL_PATH --save_directory $SAVE_PATH --cal
 
 ## Verify the quantized model
 The converted model files looks like:
+
 ```bash
 .
 |-- config.json
@@ -72,11 +73,13 @@ Run the following script to start the vLLM server with quantized model:
 :::{note}
 The value "ascend" for "--quantization" argument will be supported after [a specific PR](https://github.com/vllm-project/vllm-ascend/pull/877) is merged and released, you can cherry-pick this commit for now.
 :::
+
 ```bash
 vllm serve /home/models/QwQ-32B-w8a8  --tensor-parallel-size 4 --served-model-name "qwq-32b-w8a8" --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" \
@@ -93,7 +96,7 @@ curl http://localhost:8000/v1/completions \
 Run the following script to execute offline inference on multi-NPU with quantized model:
 
 :::{note}
-To enable quantization for ascend, quantization method must be "ascend" 
+To enable quantization for ascend, quantization method must be "ascend"
 :::
 
 ```python
@@ -131,4 +134,4 @@ for output in outputs:
 
 del llm
 clean_up()
-```
+```

docs/source/tutorials/single_node_300i.md

@@ -80,6 +80,7 @@ curl http://localhost:8000/v1/completions \
     "temperature": 0.6
   }'
 ```
+
 ::::
 
 ::::{tab-item} Qwen/Qwen2.5-7B-Instruct
@@ -318,6 +319,7 @@ if __name__ == "__main__":
 :::::
 
 Run script:
+
 ```bash
 python example.py
 ```

docs/source/tutorials/single_npu.md

@@ -66,6 +66,7 @@ for output in outputs:
     generated_text = output.outputs[0].text
     print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
 ```
+
 ::::
 
 ::::{tab-item} Eager Mode
@@ -92,6 +93,7 @@ for output in outputs:
     generated_text = output.outputs[0].text
     print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
 ```
+
 ::::
 :::::
 
@@ -131,6 +133,7 @@ docker run --rm \
 -it $IMAGE \
 vllm serve Qwen/Qwen3-8B --max_model_len 26240
 ```
+
 ::::
 
 ::::{tab-item} Eager Mode
@@ -156,6 +159,7 @@ docker run --rm \
 -it $IMAGE \
 vllm serve Qwen/Qwen3-8B --max_model_len 26240 --enforce-eager
 ```
+
 ::::
 :::::
 

docs/source/tutorials/single_npu_multimodal.md

@@ -191,4 +191,4 @@ Logs of the vllm server:
 INFO 03-12 11:16:50 logger.py:39] Received request chatcmpl-92148a41eca64b6d82d3d7cfa5723aeb: prompt: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>\nWhat is the text in the illustrate?<|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=1.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=16353, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None.
 INFO 03-12 11:16:50 engine.py:280] Added request chatcmpl-92148a41eca64b6d82d3d7cfa5723aeb.
 INFO:     127.0.0.1:54004 - "POST /v1/chat/completions HTTP/1.1" 200 OK
-```
+```

docs/source/user_guide/feature_guide/quantization.md

@@ -11,6 +11,7 @@ To quantize a model, users should install [ModelSlim](https://gitee.com/ascend/m
 Currently, only the specific tag [modelslim-VLLM-8.1.RC1.b020_001](https://gitee.com/ascend/msit/blob/modelslim-VLLM-8.1.RC1.b020_001/msmodelslim/README.md) of modelslim works with vLLM Ascend. Please do not install other version until modelslim master version is available for vLLM Ascend in the future.
 
 Install modelslim:
+
 ```bash
 git clone https://gitee.com/ascend/msit -b modelslim-VLLM-8.1.RC1.b020_001
 cd msit/msmodelslim
@@ -22,7 +23,6 @@ pip install accelerate
 
 Take [DeepSeek-V2-Lite](https://modelscope.cn/models/deepseek-ai/DeepSeek-V2-Lite) as an example, you just need to download the model, and then execute the convert command. The command is shown below. More info can be found in modelslim doc [deepseek w8a8 dynamic quantization docs](https://gitee.com/ascend/msit/blob/modelslim-VLLM-8.1.RC1.b020_001/msmodelslim/example/DeepSeek/README.md#deepseek-v2-w8a8-dynamic%E9%87%8F%E5%8C%96).
 
-
 ```bash
 cd example/DeepSeek
 python3 quant_deepseek.py --model_path {original_model_path} --save_directory {quantized_model_save_path} --device_type cpu --act_method 2 --w_bit 8 --a_bit 8  --is_dynamic True
@@ -39,6 +39,7 @@ Once convert action is done, there are two important files generated.
 2. [quant_model_description.json](https://www.modelscope.cn/models/vllm-ascend/DeepSeek-V2-Lite-W8A8/file/view/master/quant_model_description.json?status=1). All the converted weights info are recorded in this file.
 
 Here is the full converted model files:
+
 ```bash
 .
 ├── config.json
@@ -103,4 +104,4 @@ submit a issue, maybe some new models need to be adapted.
 
 ### 2. How to solve the error "Could not locate the configuration_deepseek.py"?
 
-Please convert DeepSeek series models using `modelslim-VLLM-8.1.RC1.b020_001` modelslim, this version has fixed the missing configuration_deepseek.py error.
+Please convert DeepSeek series models using `modelslim-VLLM-8.1.RC1.b020_001` modelslim, this version has fixed the missing configuration_deepseek.py error.

docs/source/user_guide/feature_guide/sleep_mode.md

@@ -6,7 +6,6 @@ Sleep Mode is an API designed to offload model weights and discard KV cache from
 
 Since the generation and training phases may employ different model parallelism strategies, it becomes crucial to free KV cache and even offload model parameters stored within vLLM during training. This ensures efficient memory utilization and avoids resource contention on the NPU.
 
-
 ## Getting started
 
 With `enable_sleep_mode=True`, the way we manage memory(malloc, free) in vllm will under a specific memory pool, during loading model and initialize kv_caches, we tag the memory as a map: `{"weight": data, "kv_cache": data}`.

docs/source/user_guide/release_notes.md

@@ -205,7 +205,7 @@ This is the first release candidate of v0.8.4 for vllm-ascend. Please follow the
 
 ### Highlights
 
-- vLLM V1 engine experimental support is included in this version. You can visit [official guide](https://docs.vllm.ai/en/latest/getting_started/v1_user_guide.html) to get more detail. By default, vLLM will fallback to V0 if V1 doesn't work, please set `VLLM_USE_V1=1` environment if you want to use V1 forcely. 
+- vLLM V1 engine experimental support is included in this version. You can visit [official guide](https://docs.vllm.ai/en/latest/getting_started/v1_user_guide.html) to get more detail. By default, vLLM will fallback to V0 if V1 doesn't work, please set `VLLM_USE_V1=1` environment if you want to use V1 forcely.
 - LoRA、Multi-LoRA And Dynamic Serving is supported now. The performance will be improved in the next release. Please follow the [official doc](https://docs.vllm.ai/en/latest/features/lora.html) for more usage information. Thanks for the contribution from China Merchants Bank. [#521](https://github.com/vllm-project/vllm-ascend/pull/521).
 - Sleep Mode feature is supported. Currently it's only work on V0 engine. V1 engine support will come soon. [#513](https://github.com/vllm-project/vllm-ascend/pull/513)
 

docs/source/user_guide/support_matrix/supported_models.md

@@ -34,7 +34,6 @@ Get the newest info here: https://github.com/vllm-project/vllm-ascend/issues/160
 | XLM-RoBERTa-based | ✅ |  |
 | Molmo | ✅ |  |
 
-
 ## Multimodal Language Models
 
 ### Generative Models