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[Doc][Misc] Improve documentation quality by revising specific content. (#8603)

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### What this PR does / why we need it?

To improve the quality of certain docs by revising specific content.

### Does this PR introduce _any_ user-facing change?

None

### How was this patch tested?

- vLLM version: v0.19.0
- vLLM main:
6f786f2c50

---------

Signed-off-by: Lucky1 <144669645+verylucky01@users.noreply.github.com>
This commit is contained in:
Lucky1
2026-04-24 15:40:41 +08:00
committed by GitHub
parent 97dbcaf919
commit bd3774d601
5 changed files with 8 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
docs/source/developer_guide/contribution/testing.md
View File

@@ -23,7 +23,7 @@ cd ~/vllm-project/
# vllm vllm-ascend
# Use mirror to speed up download
# docker pull quay.nju.edu.cn/ascend/cann:|cann_image_tag|
# docker pull m.daocloud.io/quay.io/ascend/cann:|cann_image_tag|
export IMAGE=quay.io/ascend/cann:|cann_image_tag|
docker run --rm --name vllm-ascend-ut \
-v $(pwd):/vllm-project \

4
docs/source/developer_guide/performance_and_debug/optimization_and_tuning.md
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@@ -55,7 +55,7 @@ pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
pip install modelscope pandas datasets gevent sacrebleu rouge_score pybind11 pytest
# Configure this var to speed up model download
VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
```
Please follow the [Installation Guide](https://docs.vllm.ai/projects/ascend/en/latest/installation.html) to make sure vLLM and vllm-ascend are installed correctly.
@@ -85,7 +85,7 @@ wget https://repo.oepkgs.net/ascend/pytorch/vllm/python/py311_bisheng.tar.gz
# Configure python and pip
cp ./*.so* /usr/local/lib
tar -zxvf ./py311_bisheng.* -C /usr/local/
tar -zxvf ./py311_bisheng.tar.gz -C /usr/local/
mv /usr/local/py311_bisheng/ /usr/local/python
sed -i "1c#\!/usr/local/python/bin/python3.11" /usr/local/python/bin/pip3
sed -i "1c#\!/usr/local/python/bin/python3.11" /usr/local/python/bin/pip3.11

2
docs/source/developer_guide/performance_and_debug/performance_benchmark.md
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@@ -159,7 +159,7 @@ vllm bench throughput \
If successful, you will see the following output
```shell
Processed prompts: 100%|█| 10/10 [00:03<00:00, 2.74it/s, est. speed input: 351.02 toks/s, output: 351.02 toks/s
Processed prompts: 100%|█| 10/10 [00:03<00:00, 2.74it/s, est. speed input: 351.02 toks/s, output: 351.02 toks/s]
Throughput: 2.73 requests/s, 699.93 total tokens/s, 349.97 output tokens/s
Total num prompt tokens: 1280
Total num output tokens: 1280

6
docs/source/user_guide/feature_guide/dynamic_batch.md
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@@ -24,9 +24,9 @@ We are working on further improvements and this feature will support more XPUs i
`--SLO_limits_for_dynamic_batch` is the tuning parameter (integer type) for the dynamic batch feature, larger values relax latency limitation, leading to higher effective throughput. The parameter can be selected according to the specific models or service requirements.
```python
--SLO_limits_for_dynamic_batch =-1 # default value, dynamic batch disabled.
--SLO_limits_for_dynamic_batch = 0 # baseline value for dynamic batch, dynamic batch disabled, FCFS and decode-first chunked prefilling strategy is used.
--SLO_limits_for_dynamic_batch > 0 # user-defined value for dynamic batch, dynamic batch enabled with FCFS and decode-first chunked prefilling strategy.
--SLO_limits_for_dynamic_batch = -1 # Default value; dynamic batching is disabled.
--SLO_limits_for_dynamic_batch = 0 # Baseline value for dynamic batching; dynamic batching is disabled. FCFS and decode-first chunked prefilling strategy is used.
--SLO_limits_for_dynamic_batch > 0 # User-defined positive value; dynamic batching is enabled. FCFS and decode-first chunked prefilling strategy is used.
```
### Supported Models

2
docs/source/user_guide/support_matrix/supported_features.md
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@@ -36,7 +36,7 @@ You can check the [support status of vLLM V1 Engine][v1_user_guide]. Below is th
- 🟡 Planned: Scheduled for future implementation (some may have open PRs/RFCs).
- 🔴 NO plan/Deprecated: No plan or deprecated by vLLM.
[v1_user_guide]: https://docs.vllm.ai/en/latest/getting_started/v1_user_guide.html
[v1_user_guide]: https://docs.vllm.ai/en/latest/usage/v1_guide/
[multimodal]: https://docs.vllm.ai/projects/ascend/en/latest/tutorials/models/Qwen-VL-Dense.html
[guided_decoding]: https://github.com/vllm-project/vllm-ascend/issues/177
[LoRA]: https://docs.vllm.ai/projects/ascend/en/latest/user_guide/feature_guide/lora.html