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

[Doc] Update faqs (#699)

Browse Source 
### What this PR does / why we need it?
Update faqs to make it more clear


Signed-off-by: wangli <wangli858794774@gmail.com>
This commit is contained in:
Li Wang
2025-04-28 18:48:23 +08:00
committed by GitHub
parent d0a0c81ced
commit 58f9d932d3
1 changed files with 5 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
docs/source/faqs.md
View File

@@ -30,7 +30,9 @@ You can get our containers at `Quay.io`, e.g., [<u>vllm-ascend</u>](https://quay
If you are in China, you can use `daocloud` to accelerate your downloading:
```bash
docker pull m.daocloud.io/quay.io/ascend/vllm-ascend:v0.7.3rc2
# Replace with tag you want to pull
TAG=v0.7.3rc2
docker pull m.daocloud.io/quay.io/ascend/vllm-ascend:$TAG
```
### 3. What models does vllm-ascend supports?
@@ -80,7 +82,7 @@ Currently, only 1P1D is supported by vllm. For vllm-ascend, it'll be done by [th
### 10. Does vllm-ascend support quantization method?
Currently, there is no quantization method supported in vllm-ascend originally. And the quantization supported is working in progress, w8a8 will firstly be supported.
Currently, w8a8 quantization is already supported by vllm-ascend originally on v0.8.4rc2 or heigher, If you're using vllm 0.7.3 version, w8a8 quantization is supporeted with the integration of vllm-ascend and mindie-turbo, please use `pip install vllm-ascend[mindie-turbo]`.
### 11. How to run w8a8 DeepSeek model?
@@ -96,7 +98,7 @@ If you're using vllm 0.7.3 version, this is a known progress bar display issue i
vllm-ascend is tested by functional test, performance test and accuracy test.
- **Functional test**: we added CI, includes portion of vllm's native unit tests and vllm-ascend's own unit tests，on vllm-ascend's test, we test basic functional usability for popular models, include `Qwen2.5-7B-Instruct`、 `Qwen2.5-VL-7B-Instruct`、`Qwen2.5-VL-32B-Instruct`、`QwQ-32B`.
- **Functional test**: we added CI, includes portion of vllm's native unit tests and vllm-ascend's own unit tests，on vllm-ascend's test, we test basic functionality、popular models availability and [supported features](https://vllm-ascend.readthedocs.io/en/latest/user_guide/suppoted_features.html) via e2e test
- **Performance test**: we provide [benchmark](https://github.com/vllm-project/vllm-ascend/tree/main/benchmarks) tools for end-to-end performance benchmark which can easily to re-route locally, we'll publish a perf website like [vllm](https://simon-mo-workspace.observablehq.cloud/vllm-dashboard-v0/perf) does to show the performance test results for each pull request