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

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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
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50
docs/source/tutorials/models/Qwen3-Omni-30B-A3B-Thinking.md
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@@ -16,7 +16,7 @@ Refer to [feature guide](https://docs.vllm.ai/projects/ascend/zh-cn/latest/user
### Model Weight
- `Qwen3-Omni-30B-A3B-Thinking` require 2 NPU Card(64G × 2).[Download model weight](https://modelscope.cn/models/Qwen/Qwen3-Omni-30B-A3B-Thinking)
- `Qwen3-Omni-30B-A3B-Thinking` requires 2 NPU Cards(64G × 2).[Download model weight](https://modelscope.cn/models/Qwen/Qwen3-Omni-30B-A3B-Thinking)
It is recommended to download the model weight to the shared directory of multiple nodes, such as `/root/.cache/`
### Installation
@@ -239,32 +239,32 @@ As an example, take the `gsm8k` `omnibench` `bbh` dataset as a test dataset, and
1. Refer to Using evalscope(<https://docs.vllm.ai/projects/ascend/en/latest/developer_guide/evaluation/using_evalscope.html#install-evalscope-using-pip>) for `evalscope`installation.
2. Run `evalscope` to execute the accuracy evaluation.
```bash
evalscope eval \
--model /root/.cache/modelscope/hub/models/Qwen/Qwen3-Omni-30B-A3B-Thinking \
--api-url http://localhost:8000/v1 \
--api-key EMPTY \
--eval-type server \
--datasets omni_bench, gsm8k, bbh \
--dataset-args '{"omni_bench": { "extra_params": { "use_image": true, "use_audio": false}}}' \
--eval-batch-size 1 \
--generation-config '{"max_completion_tokens": 10000, "temperature": 0.6}' \
--limit 100
```
```bash
evalscope eval \
--model /root/.cache/modelscope/hub/models/Qwen/Qwen3-Omni-30B-A3B-Thinking \
--api-url http://localhost:8000/v1 \
--api-key EMPTY \
--eval-type server \
--datasets omni_bench, gsm8k, bbh \
--dataset-args '{"omni_bench": { "extra_params": { "use_image": true, "use_audio": false}}}' \
--eval-batch-size 1 \
--generation-config '{"max_completion_tokens": 10000, "temperature": 0.6}' \
--limit 100
```
3. After execution, you can get the result, here is the result of `Qwen3-Omni-30B-A3B-Thinking` in vllm-ascend:0.13.0rc1 for reference only.
```bash
+-----------------------------+------------+----------+----------+-------+---------+---------+
| Model | Dataset | Metric | Subset | Num | Score | Cat.0 |
+=============================+============+==========+==========+=======+=========+=========+
| Qwen3-Omni-30B-A3B-Thinking | omni_bench | mean_acc | default | 100 | 0.44 | default |
+-----------------------------+------------+----------+----------+-------+---------+---------+
| Qwen3-Omni-30B-A3B-Thinking | gsm8k | mean_acc | main | 100 | 0.98 | default |
+-----------------------------+-----------+----------+----------+-------+---------+---------+
| Qwen3-Omni-30B-A3B-Thinking | bbh | mean_acc | OVERALL | 270 | 0.9148 | |
+-----------------------------+------------+----------+----------+-------+---------+---------+
```
```bash
+-----------------------------+------------+----------+----------+-------+---------+---------+
| Model | Dataset | Metric | Subset | Num | Score | Cat.0 |
+=============================+============+==========+==========+=======+=========+=========+
| Qwen3-Omni-30B-A3B-Thinking | omni_bench | mean_acc | default | 100 | 0.44 | default |
+-----------------------------+------------+----------+----------+-------+---------+---------+
| Qwen3-Omni-30B-A3B-Thinking | gsm8k | mean_acc | main | 100 | 0.98 | default |
+-----------------------------+-----------+----------+----------+-------+---------+---------+
| Qwen3-Omni-30B-A3B-Thinking | bbh | mean_acc | OVERALL | 270 | 0.9148 | |
+-----------------------------+------------+----------+----------+-------+---------+---------+
```
## Performance
@@ -272,7 +272,7 @@ evalscope eval \
Run performance evaluation of `Qwen3-Omni-30B-A3B-Thinking` as an example.
Refer to vllm benchmark for more details.
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: