vllm-project/vllm-ascend
Apr 2026 – Apr 2026
1 Month active
Languages Used
BashJSONMarkdownYAML
Technical Skills
AI model verificationdocumentationend-to-end testingmodel deployment
anning-2026
PROFILE
Anning-2026
Worked on the vllm-project/vllm-ascend repository to deliver Ascend NPU integration for the Hunyuan-A13B-Instruct model, focusing on end-to-end test configuration and deployment documentation. Verified architecture compatibility with CANN 8.5.1 on a 4-NPU GiteeAI Atlas 800 A2 setup, achieving 94.77% GSM8K accuracy in PIECEWISE (ACL Graph) mode. Used Bash and YAML to update test configurations and deployment tutorials, ensuring reproducible results and clear guidance for enterprise users. Emphasized AI model verification, model deployment, and documentation, resulting in improved reliability and measurable performance gains for vLLM 0.17.0-based workflows on Ascend hardware platforms.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
270
Activity Months1
Your Network
294 peopleSame Organization
@isrc.iscas.ac.cn13
Work History
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for vllm-project/vllm-ascend. Delivered Ascend NPU integration for Hunyuan-A13B-Instruct including end-to-end test configuration and deployment tutorial. Verified architecture compatibility (HunyuanMoEV1ForCausalLM) with CANN 8.5.1 across a 4-NPU (64G each) setup, with tests executed on GiteeAI Atlas 800 A2. Achieved model performance: GSM8K accuracy 94.77% under PIECEWISE (ACL Graph) mode. Key deployment metrics include graph compilation ~18s, weight load ~37.46 GB per NPU card, and ~529k tokens KV cache across the 4-NPU configuration. Updated tests/docs: added tests/e2e/models/configs/hunyuan_a13b.yaml and docs/source/tutorials/models/hunyuan_a13b.md. Technologies/stack: vLLM 0.17.0, vLLM-Ascend 0.17.0rc1, CANN 8.5.1, Python 3.11.6, Conda. Impact: improved Ascend integration reliability, clearer deployment guidance, and measurable accuracy/performance gains enabling faster enterprise adoption.
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for vllm-project/vllm-ascend. Delivered Ascend NPU integration for Hunyuan-A13B-Instruct including end-to-end test configuration and deployment tutorial. Verified architecture compatibility (HunyuanMoEV1ForCausalLM) with CANN 8.5.1 across a 4-NPU (64G each) setup, with tests executed on GiteeAI Atlas 800 A2. Achieved model performance: GSM8K accuracy 94.77% under PIECEWISE (ACL Graph) mode. Key deployment metrics include graph compilation ~18s, weight load ~37.46 GB per NPU card, and ~529k tokens KV cache across the 4-NPU configuration. Updated tests/docs: added tests/e2e/models/configs/hunyuan_a13b.yaml and docs/source/tutorials/models/hunyuan_a13b.md. Technologies/stack: vLLM 0.17.0, vLLM-Ascend 0.17.0rc1, CANN 8.5.1, Python 3.11.6, Conda. Impact: improved Ascend integration reliability, clearer deployment guidance, and measurable accuracy/performance gains enabling faster enterprise adoption.
April 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
BashJSONMarkdownYAML
Technical Skills
AI model verificationdocumentationend-to-end testingmodel deployment
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