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
Tonpe contributed to the microsoft/Olive repository by developing two core features over a two-month period, focusing on model preprocessing and quantization workflows. In April, Tonpe updated the Google ViT image preprocessing normalization parameters, aligning mean and standard deviation values to improve model accuracy and ensure reproducibility. In July, Tonpe delivered the Olive GPTQ Quantization Pass for QNN, integrating Hugging Face Optimum and ONNX Runtime to enable efficient, configurable quantization and streamlined model export. Working primarily in Python and leveraging skills in computer vision and model quantization, Tonpe’s work demonstrated technical depth and addressed key deployment and interoperability challenges.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
237
Activity Months2
Your Network
118 people
Work 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
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