quic/aimet
Nov 2024 – Dec 2024
2 Months active
Languages Used
PythonrstRSTnumpyonnxtorch
Technical Skills
Build ProcessClean CodeCode RefactoringDeep Learning InferenceDocumentationModel Optimization
Geunho Lee
PROFILE
Geunho Lee
Geun Lee contributed to the quic/aimet repository by developing and refining quantization workflows and improving packaging reliability. Over two months, Geun updated QuantSim code examples to use MobileNetV2, clarifying data loading and transfer learning steps for both PyTorch and ONNX, which streamlined the end-to-end quantization process from encoding to export. Geun also stabilized the documentation build by resolving cross-reference issues, enhancing onboarding and maintenance. Additional work included fixing quantization exceptions in MatMul operations and ensuring JavaScript and XML assets were correctly packaged. The work demonstrated strong skills in Python, ONNX, and technical writing, delivering maintainable, production-ready solutions.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
7Total
Bugs
3
Commits
7
Features
3
Lines of code
1,042
Activity Months2
Your Network
200 people
Work History
December 2024
3 Commits • 1 Features
Dec 1, 2024Month: 2024-12 — Focused on delivering end-to-end Quantization workflow improvements in quic/aimet and stabilizing documentation build. Key features delivered include MobileNetV2-based QuantSim code examples for PyTorch and ONNX with clear data loading, transfer learning/fine-tuning steps, and a complete quantization workflow (encodings, evaluation, and export). Major bugs fixed include documentation build cross-reference warnings resolved by correcting internal links. Overall impact: accelerated path to quantized model deployment, improved developer onboarding, and reduced support overhead. Technologies demonstrated: PyTorch, ONNX, QuantSim, end-to-end quantization workflow, transfer learning, data handling, and Sphinx/docs tooling.
3 Commits • 1 Features
Dec 1, 2024Month: 2024-12 — Focused on delivering end-to-end Quantization workflow improvements in quic/aimet and stabilizing documentation build. Key features delivered include MobileNetV2-based QuantSim code examples for PyTorch and ONNX with clear data loading, transfer learning/fine-tuning steps, and a complete quantization workflow (encodings, evaluation, and export). Major bugs fixed include documentation build cross-reference warnings resolved by correcting internal links. Overall impact: accelerated path to quantized model deployment, improved developer onboarding, and reduced support overhead. Technologies demonstrated: PyTorch, ONNX, QuantSim, end-to-end quantization workflow, transfer learning, data handling, and Sphinx/docs tooling.
December 2024
November 2024
4 Commits • 2 Features
Nov 1, 2024November 2024 performance summary for quic/aimet. Focused on stabilizing packaging, refining quantization workflows, documenting feature improvements, and removing dead code to improve maintainability and developer velocity. The changes delivered business value by reducing release risks, clarifying advanced features for inference optimization, and simplifying future development work.
November 2024
4 Commits • 2 Features
Nov 1, 2024November 2024 performance summary for quic/aimet. Focused on stabilizing packaging, refining quantization workflows, documenting feature improvements, and removing dead code to improve maintainability and developer velocity. The changes delivered business value by reducing release risks, clarifying advanced features for inference optimization, and simplifying future development work.
Activity
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Quality Metrics
Correctness94.4%
Maintainability91.4%
Architecture91.4%
Performance88.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonRSTnumpyonnxrsttorch
Technical Skills
Build ProcessClean CodeCode RefactoringData LoadingDeep Learning InferenceDocumentationMachine LearningModel ConversionModel EvaluationModel OptimizationONNXONNX QuantizationPackagingPyTorchPython
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