microsoft/olive-recipes
Jan 2026 – Mar 2026
3 Months active
Languages Used
JSONPython
Technical Skills
Data ProcessingDocumentationMachine LearningModel EvaluationModel OptimizationPython Scripting
ziyuanguo1998
PROFILE
Ziyuanguo1998
During a three-month period, Yuan Shao contributed to the microsoft/olive-recipes repository by developing end-to-end features for machine learning model evaluation and optimization. Yuan built a Whisper model inference sample with supporting evaluation scripts and documentation, streamlining onboarding and enabling reproducible performance assessment. Leveraging Python and ONNX Runtime, Yuan introduced dynamic execution provider registration, which improved evaluation throughput and reduced environment setup complexity. In March, Yuan added Stable Diffusion v1-5 support with QNN acceleration and quantization, enabling efficient on-device inference and reduced model footprint. The work demonstrated depth in deep learning, quantization, and collaborative, standards-compliant code development practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
3
Lines of code
4,924
Activity Months3
Your Network
21 peopleWork History
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly performance summary: Delivered Stable Diffusion v1-5 support with QNN acceleration and quantization for microsoft/olive-recipes, enabling faster on-device inference, lower latency, and reduced model footprint. No major bugs reported; stabilization efforts focused on ensuring robust inference paths with QNN and preserving compatibility with existing pipelines. Business impact includes expanded model support for customers, improved responsiveness of generative features, and lower compute costs on edge devices. Demonstrated expertise in on-device ML acceleration, quantization techniques, and collaborative, standards-compliant code contributions.
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly performance summary: Delivered Stable Diffusion v1-5 support with QNN acceleration and quantization for microsoft/olive-recipes, enabling faster on-device inference, lower latency, and reduced model footprint. No major bugs reported; stabilization efforts focused on ensuring robust inference paths with QNN and preserving compatibility with existing pipelines. Business impact includes expanded model support for customers, improved responsiveness of generative features, and lower compute costs on edge devices. Demonstrated expertise in on-device ML acceleration, quantization techniques, and collaborative, standards-compliant code contributions.
March 2026
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026: Delivered a feature to register ONNX Runtime execution providers dynamically for Whisper evaluation in microsoft/olive-recipes. This change enables flexible back-end selection, improves evaluation throughput, and reduces environment setup friction. A related Whisper evaluation fix was included to ensure stable and reproducible results across configurations. The work involved cross-team collaboration and includes credit for the contributing author.
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026: Delivered a feature to register ONNX Runtime execution providers dynamically for Whisper evaluation in microsoft/olive-recipes. This change enables flexible back-end selection, improves evaluation throughput, and reduces environment setup friction. A related Whisper evaluation fix was included to ensure stable and reproducible results across configurations. The work involved cross-team collaboration and includes credit for the contributing author.
January 2026
2 Commits • 1 Features
Jan 1, 2026Month: 2026-01 — microsoft/olive-recipes. Key features delivered: Whisper Model Inference Sample, Evaluation Script, and Documentation; Updated Inference sample and README with additional requirements and usage instructions. Major bugs fixed: None reported for this repo this month. Overall impact and accomplishments: Improved usability and evaluation capability for Whisper workloads, enabling faster onboarding and greater adoption by downstream teams; Documentation quality improved and code traceability enhanced via co-authored commits. Technologies/skills demonstrated: Whisper inference, evaluation scripting, Python, documentation, collaboration, and cross-functional teamwork.
2 Commits • 1 Features
Jan 1, 2026Month: 2026-01 — microsoft/olive-recipes. Key features delivered: Whisper Model Inference Sample, Evaluation Script, and Documentation; Updated Inference sample and README with additional requirements and usage instructions. Major bugs fixed: None reported for this repo this month. Overall impact and accomplishments: Improved usability and evaluation capability for Whisper workloads, enabling faster onboarding and greater adoption by downstream teams; Documentation quality improved and code traceability enhanced via co-authored commits. Technologies/skills demonstrated: Whisper inference, evaluation scripting, Python, documentation, collaboration, and cross-functional teamwork.
January 2026
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture85.0%
Performance85.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
JSONPython
Technical Skills
AI DevelopmentData ProcessingDeep LearningDocumentationMachine LearningModel EvaluationModel OptimizationPythonPython ScriptingQuantization
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