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[doc][main] Correct mistakes in doc (#4945)

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### What this PR does / why we need it?
Correct mistakes in doc

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: lilinsiman <lilinsiman@gmail.com>
This commit is contained in:
lilinsiman
2025-12-12 19:17:10 +08:00
committed by GitHub
parent f708d919f8
commit fc818f1509
9 changed files with 18 additions and 28 deletions
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6
docs/source/user_guide/configuration/additional_config.md
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@@ -33,12 +33,14 @@ The following table lists additional configuration options available in vLLM Asc
| `expert_map_path` | str | `None` | When using expert load balancing for an MoE model, an expert map path needs to be passed in. |
| `kv_cache_dtype` | str | `None` | When using the KV cache quantization method, KV cache dtype needs to be set, currently only int8 is supported. |
| `enable_shared_expert_dp` | bool | `False` | When the expert is shared in DP, it delivers better performance but consumes more memory. Currently only DeepSeek series models are supported. |
| `multistream_overlap_shared_expert` | bool | `False` | Whether to enable multistream shared expert. This option only takes effects on MoE models with shared experts. |
| `lmhead_tensor_parallel_size` | int | `None` | The custom tensor parallel size of lmhead. Restriction: Can only be used when tensor_parallel=1 |
| `oproj_tensor_parallel_size` | int | `None` | The custom tensor parallel size of oproj. |
| `multistream_overlap_shared_expert` | bool | `False` | Whether to enable multistream shared expert. This option only takes effect on MoE models with shared experts. |
| `dynamic_eplb` | bool | `False` | Whether to enable dynamic EPLB. |
| `num_iterations_eplb_update` | int | `400` | Forward iterations when EPLB begins. |
| `gate_eplb` | bool | `False` | Whether to enable EPLB only once. |
| `num_wait_worker_iterations` | int | `30` | The forward iterations when the EPLB worker will finish CPU tasks. In our test default value 30 can cover most cases. |
| `expert_map_record_path` | str | `None` | When dynamic EPLB is completed, save the current expert load heatmap to the specified path. |
| `expert_map_record_path` | str | `None` | Save the expert load calculation results to a new expert table in the specified directory. |
| `init_redundancy_expert` | int | `0` | Specify redundant experts during initialization. |
| `dump_config` | str | `None` | Configuration file path for msprobe dump(eager mode). |

2
docs/source/user_guide/feature_guide/eplb_swift_balancer.md
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@@ -76,7 +76,7 @@ vllm serve Qwen/Qwen3-235B-A22 \
- Network bandwidth must support expert redistribution traffic (≥ 10 Gbps recommended).
3. Model Compatibility:
- Only MoE models with explicit expert parallelism support (e.g., Qwen3-235B-A22) are compatible.
- Only MoE models with explicit expert parallelism support (e.g., Qwen3 MoE models) are compatible.
- Verify model architecture supports dynamic expert routing through --enable-expert-parallel.
4. Gating Configuration:

1
docs/source/user_guide/feature_guide/kv_pool.md
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@@ -113,6 +113,7 @@ python3 -m vllm.entrypoints.openai.api_server \
"kv_role": "kv_producer",
"kv_port": "20001",
"kv_connector_extra_config": {
"use_ascend_direct": true,
"prefill": {
"dp_size": 1,
"tp_size": 1

5
docs/source/user_guide/feature_guide/lora.md
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@@ -7,6 +7,11 @@ You can refer to [Supported Models](https://docs.vllm.ai/en/latest/models/suppor
You can run LoRA with ACLGraph mode now. Please refer to [Graph Mode Guide](./graph_mode.md) for a better LoRA performance.
Address for downloading models:\
base model: https://www.modelscope.cn/models/vllm-ascend/Llama-2-7b-hf/files \
lora model:
https://www.modelscope.cn/models/vllm-ascend/llama-2-7b-sql-lora-test/files
## Example
We provide a simple LoRA example here, which enables the ACLGraph mode by default.

4
docs/source/user_guide/feature_guide/quantization.md
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@@ -6,13 +6,13 @@ Since version 0.9.0rc2, the quantization feature is experimentally supported by
## Install ModelSlim
To quantize a model, you should install [ModelSlim](https://gitee.com/ascend/msit/blob/master/msmodelslim/README.md) which is the Ascend compression and acceleration tool. It is an affinity-based compression tool designed for acceleration, using compression as its core technology and built upon the Ascend platform.
To quantize a model, you should install [ModelSlim](https://gitcode.com/Ascend/msit/tree/master) which is the Ascend compression and acceleration tool. It is an affinity-based compression tool designed for acceleration, using compression as its core technology and built upon the Ascend platform.
Install ModelSlim:
```bash
# The branch(br_release_MindStudio_8.1.RC2_TR5_20260624) has been verified
git clone -b br_release_MindStudio_8.1.RC2_TR5_20260624 https://gitee.com/ascend/msit
git clone -b br_release_MindStudio_8.1.RC2_TR5_20260624 https://gitcode.com/Ascend/msit/tree/master
cd msit/msmodelslim

2
docs/source/user_guide/support_matrix/supported_features.md
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@@ -2,6 +2,8 @@
The feature support principle of vLLM Ascend is: **aligned with the vLLM**. We are also actively collaborating with the community to accelerate support.
Functional call: https://docs.vllm.ai/en/latest/features/tool_calling/
You can check the [support status of vLLM V1 Engine][v1_user_guide]. Below is the feature support status of vLLM Ascend:
| Feature | Status | Next Step |