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[main][Docs] Fix typos across documentation (#6728)

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## Summary

Fix typos and improve grammar consistency across 50 documentation files.
 
### Changes include:
- Spelling corrections (e.g., "Facotory" → "Factory", "certainty" →
"determinism")
- Grammar improvements (e.g., "multi-thread" → "multi-threaded",
"re-routed" → "re-run")
- Punctuation fixes (semicolon consistency in filter parameters)
- Code style fixes (correct flag name `--num-prompts` instead of
`--num-prompt`)
- Capitalization consistency (e.g., "python" → "Python", "ascend" →
"Ascend")
- vLLM version: v0.15.0
- vLLM main:
9562912cea

---------

Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
This commit is contained in:
Cao Yi
2026-02-13 15:50:05 +08:00
committed by GitHub
parent b6bc3d2f9d
commit 6de207de88
50 changed files with 273 additions and 272 deletions
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8
docs/source/tutorials/features/suffix_speculative_decoding.md
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@@ -15,7 +15,7 @@ This document provides step-by-step guidance on how to deploy and benchmark the
| Comprehensive Examination | agieval |
| Multi-turn Dialogue | sharegpt |
The benchmarking tool used in this tutorial is AISbench, which supports performance testing for all the datasets listed above. The final section of this tutorial presents a performance comparison between enabling and disabling Suffix Decoding under the condition of satisfying an SLO TPOT < 50ms across different datasets and concurrency levels. Validations demonstrate that the Qwen3-32B model achieves a throughput improvement of approximately 20% to 80% on various real-world datasets when Suffix Decoding is enabled.
The benchmarking tool used in this tutorial is AISBench, which supports performance testing for all the datasets listed above. The final section of this tutorial presents a performance comparison between enabling and disabling Suffix Decoding under the condition of satisfying an SLO TPOT < 50ms across different datasets and concurrency levels. Validations demonstrate that the Qwen3-32B model achieves a throughput improvement of approximately 20% to 80% on various real-world datasets when Suffix Decoding is enabled.
## **Download vllm-ascend Image**
@@ -71,14 +71,14 @@ pip install arctic-inference
## **vLLM Instance Deployment**
Use the following command to start the container service instance. Speculative inference is enabled via the `--speculative-config` parameter, where `method` is set to`suffix`. For this test, `num_speculative_tokens` is uniformly set to`3`.
Use the following command to start the container service instance. Speculative inference is enabled via the `--speculative-config` parameter, where `method` is set to `suffix`. For this test, `num_speculative_tokens` is uniformly set to `3`.
```bash
# set the NPU device number
# set the NPU device number:
export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3
# Set the operator dispatch pipeline level to 1 and disable manual memory control in ACLGraph
export TASK_QUEUE_ENABLE=1
# Enable the AIVector core to directly schedule ROCE communication
# Enable the AIVector core to directly schedule ROCE communication.
export HCCL_OP_EXPANSION_MODE="AIV"
# Enable MLP prefetch for better performance.
export VLLM_ASCEND_ENABLE_PREFETCH_MLP=1