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

9
docs/source/tutorials/models/Qwen3-235B-A22B.md
View File

@@ -18,8 +18,8 @@ Refer to [feature guide](../../user_guide/feature_guide/index.md) to get the fea
### Model Weight
- `Qwen3-235B-A22B`(BF16 version): require 1 Atlas 800 A3 (64G × 16) node, 1 Atlas 800 A2 (64G × 8) node or 2 Atlas 800 A2(32G * 8)nodes. [Download model weight](https://www.modelscope.cn/models/Qwen/Qwen3-235B-A22B)
- `Qwen3-235B-A22B-w8a8`(Quantized version): require 1 Atlas 800 A3 (64G × 16) node or 1 Atlas 800 A2 (64G × 8) node or 2 Atlas 800 A2(32G * 8)nodes. [Download model weight](https://modelscope.cn/models/vllm-ascend/Qwen3-235B-A22B-W8A8)
- `Qwen3-235B-A22B`(BF16 version): require 1 Atlas 800 A3 (64G × 16) node, 1 Atlas 800 A2 (64G × 8) node or 2 Atlas 800 A2(32G × 8)nodes. [Download model weight](https://www.modelscope.cn/models/Qwen/Qwen3-235B-A22B)
- `Qwen3-235B-A22B-w8a8`(Quantized version): require 1 Atlas 800 A3 (64G × 16) node or 1 Atlas 800 A2 (64G × 8) node or 2 Atlas 800 A2(32G × 8)nodes. [Download model weight](https://modelscope.cn/models/vllm-ascend/Qwen3-235B-A22B-W8A8)
It is recommended to download the model weight to the shared directory of multiple nodes, such as `/root/.cache/`.
@@ -191,7 +191,7 @@ vllm serve Qwen/Qwen3-235B-A22B \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--async-scheduling \
--gpu-memory-utilization 0.9 \
--gpu-memory-utilization 0.9
```
Node1
@@ -298,7 +298,7 @@ Refer to [Using AISBench for performance evaluation](../../developer_guide/evalu
Run performance evaluation of `Qwen3-235B-A22B-w8a8` as an example.
Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.
Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/benchmarking/) for more details.
There are three `vllm bench` subcommands:
@@ -440,7 +440,6 @@ vllm serve vllm-ascend/Qwen3-235B-A22B-w8a8 \
--enforce-eager \
--trust-remote-code \
--gpu-memory-utilization 0.9 \
--enforce-eager \
--no-enable-prefix-caching \
--kv-transfer-config \
'{"kv_connector": "MooncakeConnectorV1",