EngineX/xc-llm-ascend
3
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

[Doc][Misc] Comprehensive documentation cleanup and grammatical fixes (#8073)

Browse Source 
What this PR does / why we need it?
This pull request performs a comprehensive cleanup of the vLLM Ascend
documentation. It fixes numerous typos, grammatical errors, and phrasing
issues across community guidelines, developer documents, hardware
tutorials, and feature guides. Key improvements include correcting
hardware names (e.g., Atlas 300I), fixing broken links, cleaning up code
examples (removing duplicate flags and trailing commas), and improving
the clarity of technical explanations. These changes are necessary to
ensure the documentation is professional, accurate, and easy for users
to follow.

Does this PR introduce any user-facing change?
No, this PR contains documentation-only updates.

How was this patch tested?
The changes were manually reviewed for accuracy and grammatical
correctness. No functional code changes were introduced.

---------

Signed-off-by: herizhen <1270637059@qq.com>
Signed-off-by: herizhen <59841270+herizhen@users.noreply.github.com>
This commit is contained in:
herizhen
2026-04-09 15:37:57 +08:00
committed by GitHub
parent c40a387f63
commit 0d1424d81a
71 changed files with 1295 additions and 1296 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
docs/source/tutorials/features/suffix_speculative_decoding.md
View File

@@ -12,8 +12,8 @@ This document provides step-by-step guidance on how to deploy and benchmark the
| Common Sense Reasoning | ARC |
| Mathematical Reasoning | gsm8k |
| Natural Language Understanding | SuperGLUE_BoolQ |
| Comprehensive Examination | agieval |
| Multi-turn Dialogue | sharegpt |
| 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.
@@ -171,7 +171,7 @@ Below is the raw detailed test results:
| 1 | 207 | 314 | 100 | 54.1 | 18.4 | 36.1 | 26.8 | 33.4% | 49.8% | 45.6% |
| 16 | 207 | 314 | 100 | 60.0 | 229.7 | 43.5 | 303.9 | 33.4% | 38.0% | 32.3% |
| 32 | 207 | 314 | 100 | 62.7 | 396.4 | 47.8 | 507.5 | 33.4% | 31.3% | 28.0% |
| **Agieval** | | | | | | | | | | |
| **AGIEval** | | | | | | | | | | |
| 1 | 735 | 1880 | 100 | 53.1 | 18.7 | 31.8 | 34.1 | 50.3% | 66.8% | 81.9% |
| 24 | 735 | 1880 | 100 | 64.0 | 381.2 | 43.3 | 629.0 | 50.3% | 47.8% | 65.0% |
| 34 | 735 | 1880 | 100 | 70.0 | 494.6 | 50.2 | 768.4 | 50.3% | 39.4% | 55.3% |