[Doc][Misc] Comprehensive documentation cleanup and grammatical fixes (#8073)
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>
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@@ -11,15 +11,16 @@ We are working on further improvements and this feature will support more XPUs i
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### Prerequisites
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1. Dynamic batch now depends on an offline cost model saved in a lookup table to refine the token budget. The lookup table is saved in a '.csv' file, which should be first downloaded from [here](https://vllm-ascend.obs.cn-north-4.myhuaweicloud.com/vllm-ascend/dynamic_batch_scheduler/A2-B3-BLK128.csv), renamed, and saved to the path `vllm_ascend/core/profile_table.csv`
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1. Dynamic batch now depends on an offline cost model saved in a lookup table to refine the token budget. The lookup table is saved in a '.csv' file, which should be first downloaded from [A2-B3-BLK128.csv](https://vllm-ascend.obs.cn-north-4.myhuaweicloud.com/vllm-ascend/dynamic_batch_scheduler/A2-B3-BLK128.csv), renamed, and saved to the path `vllm_ascend/core/profile_table.csv`
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2. `Pandas` is needed to load the lookup table, in case pandas is not installed.
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```bash
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```bash
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pip install pandas
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```
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### Tuning Parameters
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`--SLO_limits_for_dynamic_batch` is the tuning parameter (integer type) for the dynamic batch feature, larger values impose more constraints on the latency limitation, leading to higher effective throughput. The parameter can be selected according to the specific models or service requirements.
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```python
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