CodeLinaro/onnxruntime
Mar 2025 – Jun 2025
2 Months active
Languages Used
C++Python
Technical Skills
C++ developmentMachine LearningModel OptimizationNeural NetworksQNNPython Programming
chuteng-quic
PROFILE
Chuteng-quic
Chuteng contributed to the CodeLinaro/onnxruntime repository by enhancing the QNN Execution Provider, expanding its inference capabilities and enabling offline deployment through support for the Sum operator and Lora Adapter Binary. Using C++ and Python, Chuteng implemented configuration parsing and runtime application to streamline offline workflows, improving deployment flexibility across devices. In addition, Chuteng addressed ONNX model generation stability by replacing the Upsample operator with Resize in the quantization path, ensuring compatibility with opset 11 and reducing model errors. The work demonstrated depth in model optimization and data processing, resulting in more reliable and production-ready machine learning pipelines.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
1
Lines of code
284
Activity Months2
Your Network
207 people
Work History
June 2025
2 Commits
Jun 1, 2025June 2025 monthly summary for CodeLinaro/onnxruntime: Key features delivered and bugs fixed, with business value and technical impact. Focused on ONNX model generation stability and compatibility across opset 11, improving reliability of downstream inference pipelines and reducing model generation errors.
2 Commits
Jun 1, 2025June 2025 monthly summary for CodeLinaro/onnxruntime: Key features delivered and bugs fixed, with business value and technical impact. Focused on ONNX model generation stability and compatibility across opset 11, improving reliability of downstream inference pipelines and reducing model generation errors.
June 2025
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for CodeLinaro/onnxruntime: Delivered QNN Execution Provider (QNN-EP) enhancements expanding inference capabilities and offline deployment readiness. Core deliveries include Sum operator support (2 inputs) and Lora Adapter Binary support (offline context binaries) with configuration parsing and runtime application. These changes broaden QNN coverage, improve runtime configurability, and enable offline workflows, contributing to performance and deployment flexibility across supported devices.
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for CodeLinaro/onnxruntime: Delivered QNN Execution Provider (QNN-EP) enhancements expanding inference capabilities and offline deployment readiness. Core deliveries include Sum operator support (2 inputs) and Lora Adapter Binary support (offline context binaries) with configuration parsing and runtime application. These changes broaden QNN coverage, improve runtime configurability, and enable offline workflows, contributing to performance and deployment flexibility across supported devices.
Activity
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Quality Metrics
Correctness100.0%
Maintainability85.0%
Architecture90.0%
Performance85.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++ developmentMachine LearningModel OptimizationNeural NetworksPython ProgrammingPython programmingQNNQuantizationdata processingmachine learningmodel optimization
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