microsoft/Olive
Apr 2025 – Jul 2025
2 Months active
Languages Used
Python
Technical Skills
Computer VisionMachine LearningHugging Face OptimumModel QuantizationONNX RuntimeQNN
Gokul Tonpe
PROFILE
Gokul Tonpe
Contributed to the microsoft/Olive repository by developing two core features over a two-month period, focusing on computer vision and model quantization workflows. Updated Google ViT image preprocessing by aligning normalization parameters to improve model accuracy, ensuring traceability and reproducibility through comprehensive documentation and commit history. Later, implemented a GPTQ quantization pass for QNN, integrating Hugging Face Optimum and ONNX Runtime to enable efficient model quantization and export with configurable parameters. All work was delivered in Python, leveraging expertise in machine learning, ONNX Runtime, and QNN integration. The contributions emphasized maintainability, interoperability, and streamlined deployment for advanced model inference scenarios.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
237
Activity Months2
Your Network
263 peopleWork History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for microsoft/Olive. Focused on delivering a quantization feature for QNN integration. Delivered the Olive GPTQ Quantization Pass for QNN with Hugging Face Optimum integration and ONNX Runtime export, enabling configurable quantization settings and optimized kernels. This work supports efficient model quantization, interoperability with QNN, and streamlined deployment via ONNX Runtime. No major bugs fixed this month; activities centered on feature delivery and integration, laying groundwork for improved inference performance and broader adoption.
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for microsoft/Olive. Focused on delivering a quantization feature for QNN integration. Delivered the Olive GPTQ Quantization Pass for QNN with Hugging Face Optimum integration and ONNX Runtime export, enabling configurable quantization settings and optimized kernels. This work supports efficient model quantization, interoperability with QNN, and streamlined deployment via ONNX Runtime. No major bugs fixed this month; activities centered on feature delivery and integration, laying groundwork for improved inference performance and broader adoption.
July 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for microsoft/Olive focusing on key delivers, impact, and technical proficiency.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for microsoft/Olive focusing on key delivers, impact, and technical proficiency.
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Computer VisionHugging Face OptimumMachine LearningModel QuantizationONNX RuntimeQNN
Repositories Contributed To
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