microsoft/Olive
Jul 2025 – Oct 2025
4 Months active
Languages Used
C++PythonMarkdown
Technical Skills
AIMETModel OptimizationONNXPythonQuantizationData Engineering
Tuttle, Michael
PROFILE
Tuttle, Michael
Over four months, Matt Tuttle developed and integrated advanced quantization features for the microsoft/Olive repository, focusing on model optimization for deployment. He engineered ONNX model quantization passes using Python and ONNX Runtime, enabling configurable low-precision formats and supporting techniques like SeqMSE, LPBQ, and AdaRound. His work included selective operator exclusion, LLM-augmented dataloaders, and pre-quantized model workflows, all validated through comprehensive testing. Matt unified AIMET-based quantization into Olive’s CLI, improved documentation, and enhanced usability for end-to-end quantization flows. This engineering effort deepened Olive’s quantization capabilities, supporting broader hardware compatibility and more accurate, efficient machine learning model deployment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
9Total
Bugs
0
Commits
9
Features
4
Lines of code
1,019
Activity Months4
Your Network
118 people
Work History
October 2025
3 Commits • 1 Features
Oct 1, 2025October 2025: Delivered AIMET quantization integration for Olive, unifying user-facing quantization capabilities and improving performance, usability, and testing. Enabled AIMET-based quantization techniques (SeqMSE, LPBQ, AdaRound) through the Olive ONNX quantization path and integrated AIMET into the Olive CLI, with comprehensive user documentation for AimetQuantization. This work strengthens quantization workflows and positions Olive for broader hardware- and accuracy-focused optimizations.
3 Commits • 1 Features
Oct 1, 2025October 2025: Delivered AIMET quantization integration for Olive, unifying user-facing quantization capabilities and improving performance, usability, and testing. Enabled AIMET-based quantization techniques (SeqMSE, LPBQ, AdaRound) through the Olive ONNX quantization path and integrated AIMET into the Olive CLI, with comprehensive user documentation for AimetQuantization. This work strengthens quantization workflows and positions Olive for broader hardware- and accuracy-focused optimizations.
October 2025
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025: Delivered Adaround support for AIMET quantization pass in microsoft/Olive. Implemented Adaround class (implements _AimetTechnique), integrated into the AimetQuantization pass, added data-configuration support for data-dependent techniques, and created unit tests to verify Adaround functionality. Commit b5ad1b7ffa68b81df6ac5eb6a9f26d094382ddd0. This work improves quantization accuracy and deployment reliability.
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025: Delivered Adaround support for AIMET quantization pass in microsoft/Olive. Implemented Adaround class (implements _AimetTechnique), integrated into the AimetQuantization pass, added data-configuration support for data-dependent techniques, and created unit tests to verify Adaround functionality. Commit b5ad1b7ffa68b81df6ac5eb6a9f26d094382ddd0. This work improves quantization accuracy and deployment reliability.
August 2025
4 Commits • 1 Features
Aug 1, 2025In August 2025, delivered significant AIMET quantization pass enhancements in microsoft/Olive, expanding deployment-ready quantization capabilities across selective operator exclusion, LLM-augmented dataloaders, pre-quantized ONNX workflow, and LPBQ with multi-technique support. These changes improve deployment flexibility, reduce errors, and enable broader hardware compatibility, accelerating model readiness for production environments.
4 Commits • 1 Features
Aug 1, 2025In August 2025, delivered significant AIMET quantization pass enhancements in microsoft/Olive, expanding deployment-ready quantization capabilities across selective operator exclusion, LLM-augmented dataloaders, pre-quantized ONNX workflow, and LPBQ with multi-technique support. These changes improve deployment flexibility, reduce errors, and enable broader hardware compatibility, accelerating model readiness for production environments.
August 2025
July 2025
1 Commits • 1 Features
Jul 1, 2025Month: 2025-07 | Overview: Focused on delivering impactful model efficiency improvements for the Olive project. Key feature delivered: ONNX Model Quantization Pass (AIMET-ONNX) integrated into Microsoft Olive, enabling quantization of weights to INT4/INT8/INT16 and activations to UINT8/UINT16/FP16 with configurable schemes, calibration data, and custom AIMET configurations, including validation and testing across scenarios. No major bugs fixed this month. Overall impact: enables smaller, faster quantized models suitable for deployment across edges and inference servers, contributing to cost savings and performance gains. Technologies/skills demonstrated: ONNX, AIMET-ONNX integration, quantization techniques, calibration workflows, configuration-driven automation, cross-scenario validation, and collaboration through code reviews and commits.
July 2025
1 Commits • 1 Features
Jul 1, 2025Month: 2025-07 | Overview: Focused on delivering impactful model efficiency improvements for the Olive project. Key feature delivered: ONNX Model Quantization Pass (AIMET-ONNX) integrated into Microsoft Olive, enabling quantization of weights to INT4/INT8/INT16 and activations to UINT8/UINT16/FP16 with configurable schemes, calibration data, and custom AIMET configurations, including validation and testing across scenarios. No major bugs fixed this month. Overall impact: enables smaller, faster quantized models suitable for deployment across edges and inference servers, contributing to cost savings and performance gains. Technologies/skills demonstrated: ONNX, AIMET-ONNX integration, quantization techniques, calibration workflows, configuration-driven automation, cross-scenario validation, and collaboration through code reviews and commits.
Activity
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Quality Metrics
Correctness86.8%
Maintainability82.2%
Architecture86.8%
Performance73.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++MarkdownPython
Technical Skills
AIMETCLI DevelopmentData EngineeringDocumentationFull Stack DevelopmentMachine LearningMachine Learning OptimizationModel OptimizationONNXONNX RuntimePythonQuantizationTesting
Repositories Contributed To
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