vllm-project/vllm-omni
May 2026 – May 2026
1 Month active
Languages Used
Python
Technical Skills
NPU developmentNPU optimizationmachine learningmodel inferencemodel optimizationquantization
hxhhhlalala
PROFILE
Hxhhhlalala
Developed MXFP quantization support for Wan2.2 models on Ascend NPU within the vllm-omni repository, focusing on both online and offline quantization paths for W8A8 MXFP8 and W4A4 MXFP4 configurations. Leveraged Python and expertise in NPU development, model inference, and quantization to integrate these features, enhancing deployment efficiency and inference performance on Ascend hardware. The work included comprehensive updates to documentation, user guides, and examples, as well as the addition of tests to validate new quantization workflows. This contribution improved model serving speed and cost-effectiveness, supporting a more robust and optimized machine learning deployment pipeline.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
4,849
Activity Months1
Your Network
202 peopleWork History
May 2026
2 Commits • 1 Features
May 1, 2026May 2026 monthly summary for vllm-omni: Implemented MXFP quantization support for Wan2.2 models on Ascend NPU (W8A8 MXFP8; W4A4 MXFP4), enabling online and offline quantization paths. Delivered comprehensive documentation updates, examples, user guides, and tests to validate functionality. This work enhances deployment efficiency and inference performance on Ascend hardware, supporting faster, more cost-effective model serving.
2 Commits • 1 Features
May 1, 2026May 2026 monthly summary for vllm-omni: Implemented MXFP quantization support for Wan2.2 models on Ascend NPU (W8A8 MXFP8; W4A4 MXFP4), enabling online and offline quantization paths. Delivered comprehensive documentation updates, examples, user guides, and tests to validate functionality. This work enhances deployment efficiency and inference performance on Ascend hardware, supporting faster, more cost-effective model serving.
May 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance90.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
NPU developmentNPU optimizationmachine learningmodel inferencemodel optimizationquantization
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