From 58f9d932d36856ad41ad1463b44e11b374ee9e8a Mon Sep 17 00:00:00 2001 From: Li Wang Date: Mon, 28 Apr 2025 18:48:23 +0800 Subject: [PATCH] [Doc] Update faqs (#699) ### What this PR does / why we need it? Update faqs to make it more clear Signed-off-by: wangli --- docs/source/faqs.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/docs/source/faqs.md b/docs/source/faqs.md index 1b869eb..fbe9883 100644 --- a/docs/source/faqs.md +++ b/docs/source/faqs.md @@ -30,7 +30,9 @@ You can get our containers at `Quay.io`, e.g., [vllm-ascend](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