EngineX/xc-llm-ascend
3
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

[Doc][releases/v0.18.0] fix documentation error or non-standard description (#8626)

Browse Source 
### What this PR does / why we need it?
fix documentation error or non-standard description in releases/v0.18.0
branch
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Documentation check.

---------

Signed-off-by: linfeng-yuan <1102311262@qq.com>
This commit is contained in:
linfeng-yuan
2026-04-23 18:55:44 +08:00
committed by GitHub
parent 786eaf8b07
commit 5c048a9b71
15 changed files with 39 additions and 40 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
docs/source/tutorials/models/Qwen3-235B-A22B.md
View File

@@ -127,7 +127,7 @@ vllm serve vllm-ascend/Qwen3-235B-A22B-w8a8 \
- [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B#processing-long-texts) originally only supports 40960 context(max_position_embeddings). If you want to use it and its related quantization weights to run long seqs (such as 128k context), it is required to use yarn rope-scaling technique.
- For vLLM version same as or new than `v0.12.0`, use parameter: `--hf-overrides '{"rope_parameters": {"rope_type":"yarn","rope_theta":1000000,"factor":4,"original_max_position_embeddings":32768}}' \`.
- For vllm version below `v0.12.0`, use parameter: `--rope_scaling '{"rope_type":"yarn","factor":4,"original_max_position_embeddings":32768}' \`.
- For vllm version below `v0.12.0`, use parameter: `--rope-scaling '{"rope_type":"yarn","factor":4,"original_max_position_embeddings":32768}' \`.
If you are using weights like [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507) which originally supports long contexts, there is no need to add this parameter.
The parameters are explained as follows:
@@ -150,7 +150,7 @@ The parameters are explained as follows:
### Multi-node Deployment with MP (Recommended)
Assume you have Atlas 800 A3 (64G*16) nodes (or 2* A2), and want to deploy the `Qwen3-VL-235B-A22B-Instruct` model across multiple nodes.
Assume you have Atlas 800 A3 (64G*16) nodes (or 2* A2), and want to deploy the `Qwen3-235B-A22B-Instruct` model across multiple nodes.
Node 0
@@ -282,7 +282,7 @@ Here are two accuracy evaluation methods.
1. Refer to [Using AISBench](../../developer_guide/evaluation/using_ais_bench.md) for details.
2. After execution, you can get the result, here is the result of `Qwen3-235B-A22B-w8a8` in `vllm-ascend:0.11.0rc0` for reference only.
2. After execution, you can get the result, here is the result of `Qwen3-235B-A22B-w8a8` in `vllm-ascend:v0.11.0rc0` for reference only.
| dataset | version | metric | mode | vllm-api-general-chat |
|----- | ----- | ----- | ----- | -----|
@@ -310,7 +310,7 @@ Take the `serve` as an example. Run the code as follows.
```shell
export VLLM_USE_MODELSCOPE=true
vllm bench serve --model vllm-ascend/Qwen3-235B-A22B-w8a8 --dataset-name random --random-input 200 --num-prompts 200 --request-rate 1 --save-result --result-dir ./
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 ./
```
After about several minutes, you can get the performance evaluation result.
@@ -589,7 +589,7 @@ vllm serve vllm-ascend/Qwen3-235B-A22B-w8a8 \
PD proxy:
```shell
python load_balance_proxy_server_example.py --port 12347 --prefiller-hosts prefill_node_1_ip --prefiller-port 8000 --decoder-hosts decode_node_1_ip --decoder-ports 8000
python load_balance_proxy_server_example.py --port 12347 --prefiller-hosts prefill_node_1_ip --prefiller-ports 8000 --decoder-hosts decode_node_1_ip --decoder-ports 8000
```
Benchmark scripts: