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[Doc][v0.18.0] Fix documentation formatting and improve code examples (#8701)

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### What this PR does / why we need it?
This PR fixes various documentation issues and improves code examples
throughout the project.

Signed-off-by: MrZ20 <2609716663@qq.com>
This commit is contained in:
SILONG ZENG
2026-04-28 09:01:25 +08:00
committed by GitHub
parent 9a0b786f2b
commit 2e2aaa2fae
38 changed files with 205 additions and 188 deletions
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2
docs/source/tutorials/models/DeepSeek-R1.md
View File

@@ -302,7 +302,7 @@ There are three `vllm bench` subcommands:
Take the `serve` as an example. Run the code as follows.
```shell
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
vllm bench serve --model path/DeepSeek-R1-W8A8 --dataset-name random --random-input 200 --num-prompts 200 --request-rate 1 --save-result --result-dir ./
```

2
docs/source/tutorials/models/DeepSeek-V3.2.md
View File

@@ -943,7 +943,7 @@ There are three `vllm bench` subcommands:
Take the `serve` as an example. Run the code as follows.
```shell
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
vllm bench serve --model /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot --dataset-name random --random-input 200 --num-prompts 200 --request-rate 1 --save-result --result-dir ./
```

2
docs/source/tutorials/models/DeepSeekOCR2.md
View File

@@ -93,7 +93,7 @@ vllm serve /root/.cache/DeepSeek-OCR-2 \
--trust-remote-code \
--tensor-parallel-size 1 \
--port 1055 \
--max_model_len 8192 \
--max-model-len 8192 \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.8 \
--allowed-local-media-path / \

2
docs/source/tutorials/models/Kimi-K2.5.md
View File

@@ -784,7 +784,7 @@ There are three `vllm bench` subcommands:
Take the `serve` as an example. Run the code as follows.
```shell
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
vllm bench serve --model Eco-Tech/Kimi-K2.5-w4a8 --dataset-name random --random-input 1024 --num-prompts 200 --request-rate 1 --save-result --result-dir ./
```

8
docs/source/tutorials/models/PaddleOCR-VL.md
View File

@@ -72,7 +72,7 @@ Run the following script to start the vLLM server on single 910B4:
```shell
#!/bin/sh
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
export MODEL_PATH="PaddlePaddle/PaddleOCR-VL"
export TASK_QUEUE_ENABLE=1
export CPU_AFFINITY_CONF=1
@@ -97,11 +97,11 @@ Run the following script to start the vLLM server on single Atlas 300 inference
```shell
#!/bin/sh
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
export MODEL_PATH="PaddlePaddle/PaddleOCR-VL"
vllm serve ${MODEL_PATH} \
--max_model_len 16384 \
--max-model-len 16384 \
--served-model-name PaddleOCR-VL-0.9B \
--trust-remote-code \
--no-enable-prefix-caching \
@@ -112,7 +112,7 @@ vllm serve ${MODEL_PATH} \
```
:::{note}
The `--max_model_len` option is added to prevent errors when generating the attention operator mask on the Atlas 300 inference products.
The `--max-model-len` option is added to prevent errors when generating the attention operator mask on the Atlas 300 inference products.
:::
::::

6
docs/source/tutorials/models/Qwen-VL-Dense.md
View File

@@ -323,12 +323,12 @@ Run docker container to start the vLLM server on single-NPU:
:substitutions:
vllm serve Qwen/Qwen3-VL-8B-Instruct \
--dtype bfloat16 \
--max_model_len 16384 \
--max-model-len 16384 \
--max-num-batched-tokens 16384
```
:::{note}
Add `--max_model_len` option to avoid ValueError that the Qwen3-VL-8B-Instruct model's max seq len (256000) is larger than the maximum number of tokens that can be stored in KV cache. This will differ with different NPU series based on the on-chip memory size. Please modify the value according to a suitable value for your NPU series.
Add `--max-model-len` option to avoid ValueError that the Qwen3-VL-8B-Instruct model's max seq len (256000) is larger than the maximum number of tokens that can be stored in KV cache. This will differ with different NPU series based on the on-chip memory size. Please modify the value according to a suitable value for your NPU series.
:::
If your service start successfully, you can see the info shown below:
@@ -415,7 +415,7 @@ vllm serve Qwen/Qwen2.5-VL-32B-Instruct \
```
:::{note}
Add `--max_model_len` option to avoid ValueError that the Qwen2.5-VL-32B-Instruct model's max_model_len (128000) is larger than the maximum number of tokens that can be stored in KV cache. This will differ with different NPU series base on the on-chip memory size. Please modify the value according to a suitable value for your NPU series.
Add `--max-model-len` option to avoid ValueError that the Qwen2.5-VL-32B-Instruct model's max_model_len (128000) is larger than the maximum number of tokens that can be stored in KV cache. This will differ with different NPU series base on the on-chip memory size. Please modify the value according to a suitable value for your NPU series.
:::
If your service start successfully, you can see the info shown below:

4
docs/source/tutorials/models/Qwen2.5-Omni.md
View File

@@ -74,7 +74,7 @@ The environment variable `LOCAL_MEDIA_PATH` which allows API requests to read lo
:::
```bash
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
export MODEL_PATH="Qwen/Qwen2.5-Omni-7B"
export LOCAL_MEDIA_PATH=$HOME/.cache/vllm/assets/vllm_public_assets/
@@ -104,7 +104,7 @@ VLLM_TARGET_DEVICE=empty pip install -v ".[audio]"
#### Multiple NPU (Qwen2.5-Omni-7B)
```bash
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
export MODEL_PATH=Qwen/Qwen2.5-Omni-7B
export LOCAL_MEDIA_PATH=$HOME/.cache/vllm/assets/vllm_public_assets/
export DP_SIZE=8

16
docs/source/tutorials/models/Qwen3-235B-A22B.md
View File

@@ -95,7 +95,7 @@ Run the following script to execute online 128k inference.
```shell
#!/bin/sh
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
# To reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_BUFFSIZE=512
@@ -157,7 +157,7 @@ Node 0
```shell
#!/bin/sh
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
# To reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
# this obtained through ifconfig
@@ -199,7 +199,7 @@ Node1
```shell
#!/bin/sh
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
# To reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
# this obtained through ifconfig
@@ -309,7 +309,7 @@ There are three `vllm bench` subcommands:
Take the `serve` as an example. Run the code as follows.
```shell
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
vllm bench serve --model vllm-ascend/Qwen3-235B-A22B-w8a8 --dataset-name random --random-input-len 200 --num-prompts 200 --request-rate 1 --save-result --result-dir ./
```
@@ -335,7 +335,7 @@ Example server scripts:
```shell
#!/bin/sh
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
# To reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_BUFFSIZE=512
@@ -408,7 +408,7 @@ export TP_SOCKET_IFNAME=${ifname}
export HCCL_SOCKET_IFNAME=${ifname}
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
# To reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_BUFFSIZE=512
@@ -470,7 +470,7 @@ export TP_SOCKET_IFNAME=${ifname}
export HCCL_SOCKET_IFNAME=${ifname}
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
# To reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_BUFFSIZE=1024
@@ -534,7 +534,7 @@ export TP_SOCKET_IFNAME=${ifname}
export HCCL_SOCKET_IFNAME=${ifname}
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
# To reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_BUFFSIZE=1024

2
docs/source/tutorials/models/Qwen3-8B-W4A8.md
View File

@@ -93,7 +93,7 @@ The converted model files look like:
Run the following script to start the vLLM server with the quantized model:
```bash
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
export MODEL_PATH=vllm-ascend/Qwen3-8B-W4A8
vllm serve ${MODEL_PATH} --served-model-name "qwen3-8b-w4a8" --max-model-len 4096 --quantization ascend
```

2
docs/source/tutorials/models/Qwen3-Coder-30B-A3B.md
View File

@@ -64,7 +64,7 @@ For an Atlas A2 with 64 GB of NPU card memory, tensor-parallel-size should be at
```shell
#!/bin/sh
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --served-model-name qwen3-coder --tensor-parallel-size 4 --enable_expert_parallel
```

2
docs/source/tutorials/models/Qwen3-Next.md
View File

@@ -163,7 +163,7 @@ There are three `vllm bench` subcommands:
Take the `serve` as an example. Run the code as follows.
```shell
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
vllm bench serve --model Qwen/Qwen3-Next-80B-A3B-Instruct --dataset-name random --random-input 200 --num-prompts 200 --request-rate 1 --save-result --result-dir ./
```

6
docs/source/tutorials/models/Qwen3-VL-235B-A22B-Instruct.md
View File

@@ -94,7 +94,7 @@ Node 0
```shell
#!/bin/sh
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
# To reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
# this obtained through ifconfig
@@ -137,7 +137,7 @@ Node1
```shell
#!/bin/sh
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
# To reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
# this is obtained through ifconfig
@@ -269,7 +269,7 @@ There are three `vllm bench` subcommands:
Take the `serve` as an example. Run the code as follows.
```shell
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
vllm bench serve --model Qwen/Qwen3-VL-235B-A22B-Instruct --dataset-name random --random-input 200 --num-prompts 200 --request-rate 1 --save-result --result-dir ./
```

4
docs/source/tutorials/models/Qwen3.5-27B.md
View File

@@ -94,7 +94,7 @@ Run the following script to execute online 128k inference.
```shell
#!/bin/sh
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
# To reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_BUFFSIZE=512
@@ -190,7 +190,7 @@ There are three `vllm bench` subcommands:
Take the `serve` as an example. Run the code as follows.
```shell
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
vllm bench serve --model Eco-Tech/Qwen3.5-27B-w8a8-mtp --dataset-name random --random-input 200 --num-prompts 200 --request-rate 1 --save-result --result-dir ./
```

8
docs/source/tutorials/models/Qwen3.5-397B-A17B.md
View File

@@ -94,7 +94,7 @@ Run the following script to execute online 128k inference On 1 Atlas 800 A3(64G*
```shell
#!/bin/sh
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
# To reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_OP_EXPANSION_MODE="AIV"
@@ -157,7 +157,7 @@ Node 0
```shell
#!/bin/sh
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
# To reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
# this obtained through ifconfig
@@ -203,7 +203,7 @@ Node1
```shell
#!/bin/sh
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
# To reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
# this obtained through ifconfig
@@ -595,7 +595,7 @@ There are three `vllm bench` subcommands:
Take the `serve` as an example. Run the code as follows.
```shell
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_MODELSCOPE=True
vllm bench serve --model Eco-Tech/Qwen3.5-397B-A17B-w8a8-mtp --dataset-name random --random-input 200 --num-prompts 200 --request-rate 1 --save-result --result-dir ./
```