zhangxinyuehfad 0060886a37 [CI]Update accuracy report test (#1288)
### What this PR does / why we need it?
Update accuracy report test
1. Add Record commit hashes and GitHub links for both vllm and
vllm-ascend in accuracy reports
2. Add accuracy result verification checks to ensure output correctness
3. Creat PR via forked repository workflow

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
dense-accuracy-test:
https://github.com/vllm-project/vllm-ascend/actions/runs/15745619485
create pr via forked repository workflow:
https://github.com/zhangxinyuehfad/vllm-ascend/actions/runs/15747013719/job/44385134080
accuracy report pr:
https://github.com/vllm-project/vllm-ascend/pull/1292

Currently, the accuracy report used is old and needs to be merged into
pr, retest, update new report, then close #1292 .


Signed-off-by: hfadzxy <starmoon_zhang@163.com>
2025-06-25 14:10:34 +08:00
2025-06-25 14:07:14 +08:00
2025-06-25 14:07:14 +08:00
2025-02-05 10:53:12 +08:00
2025-01-29 02:44:13 -08:00
2025-04-01 09:25:33 +08:00

vllm-ascend

vLLM Ascend Plugin

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Overview

vLLM Ascend (vllm-ascend) is a community maintained hardware plugin for running vLLM seamlessly on the Ascend NPU.

It is the recommended approach for supporting the Ascend backend within the vLLM community. It adheres to the principles outlined in the [RFC]: Hardware pluggable, providing a hardware-pluggable interface that decouples the integration of the Ascend NPU with vLLM.

By using vLLM Ascend plugin, popular open-source models, including Transformer-like, Mixture-of-Expert, Embedding, Multi-modal LLMs can run seamlessly on the Ascend NPU.

Prerequisites

  • Hardware: Atlas 800I A2 Inference series, Atlas A2 Training series
  • OS: Linux
  • Software:
    • Python >= 3.9, < 3.12
    • CANN >= 8.1.RC1
    • PyTorch >= 2.5.1, torch-npu >= 2.5.1.post1.dev20250619
    • vLLM (the same version as vllm-ascend)

Getting Started

Please refer to QuickStart and Installation for more details.

Contributing

See CONTRIBUTING for more details, which is a step-by-step guide to help you set up development environment, build and test.

We welcome and value any contributions and collaborations:

Branch

vllm-ascend has main branch and dev branch.

  • main: main branchcorresponds to the vLLM main branch, and is continuously monitored for quality through Ascend CI.
  • vX.Y.Z-dev: development branch, created with part of new releases of vLLM. For example, v0.7.3-dev is the dev branch for vLLM v0.7.3 version.

Below is maintained branches:

Branch Status Note
main Maintained CI commitment for vLLM main branch and vLLM 0.9.x branch
v0.7.1-dev Unmaintained Only doc fixed is allowed
v0.7.3-dev Maintained CI commitment for vLLM 0.7.3 version

Please refer to Versioning policy for more details.

Weekly Meeting

License

Apache License 2.0, as found in the LICENSE file.

Description
XC-LLM: A Specially Optimized LLM Inference Engine for ModelHub XC
Readme Apache-2.0 8.6 MiB
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