microsoft/onnxruntime-genai
Jan 2025 – Mar 2025
2 Months active
Languages Used
C++Python
Technical Skills
C++Machine LearningModel DevelopmentModel configuration managementPythonPython scripting
shobrienDMA
PROFILE
Shobriendma
Worked on the microsoft/onnxruntime-genai repository, delivering two major features over two months. Developed AMD OLMo model support by introducing a dedicated OLMoModel class and updating builder logic, which expanded model compatibility and improved deployment pipelines. Enhanced configuration management in Python by correcting model path handling, reducing initialization errors and increasing reliability for QA workflows. Later, implemented Quark quantized model support, enabling per-layer quantization group sizes and efficient processing of hf_format exports. Leveraged C++, Python, and deep learning techniques to optimize model performance, memory usage, and flexibility, supporting scalable GenAI workloads across diverse hardware and constrained environments.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
2
Lines of code
352
Activity Months2
Your Network
1620 peopleSame Organization
@amd.com1561
7b30f3f5e26d48061f873d04cc7e1d1f_amdengMember
GunaShekar, AjayMember
aasbodduMember
Abdul Lateef AttarMember
Acim MaravicMember
Adam DickinMember
Pryor, AdamMember
Pryor, AdamMember
Adel JoharMember
Work History
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for microsoft/onnxruntime-genai: Delivered Quark Quantized Models Support in ONNX Runtime GenAI, enabling processing of hf_format exports and per-layer quantization group sizes configured by Quark. Major bugs fixed: none reported. Overall impact: improved inference efficiency, reduced memory footprint, and increased flexibility for GenAI workloads, enabling cost-effective scaling and broader model interoperability across constrained environments. Technologies demonstrated: ONNX Runtime GenAI, Quark quantization, hf_format integration, and configurable per-layer quantization.
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for microsoft/onnxruntime-genai: Delivered Quark Quantized Models Support in ONNX Runtime GenAI, enabling processing of hf_format exports and per-layer quantization group sizes configured by Quark. Major bugs fixed: none reported. Overall impact: improved inference efficiency, reduced memory footprint, and increased flexibility for GenAI workloads, enabling cost-effective scaling and broader model interoperability across constrained environments. Technologies demonstrated: ONNX Runtime GenAI, Quark quantization, hf_format integration, and configurable per-layer quantization.
March 2025
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for microsoft/onnxruntime-genai. Key features delivered include AMD OLMo model support in ONNX Runtime, introduction of an OLMoModel class, and updates to the builder logic, complemented by documentation improvements. Major bugs fixed include correcting the model configuration loading in model-qa.py by switching from model to model_path to ensure the correct model path is used when loading configurations. Overall impact: increased reliability of configuration loading, expanded model compatibility with AMD hardware, and a clearer, more maintainable codebase for QA/model deployment pipelines. Technologies/skills demonstrated include ONNX Runtime integration, Python, model configuration handling, and documentation/testing enhancements.
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for microsoft/onnxruntime-genai. Key features delivered include AMD OLMo model support in ONNX Runtime, introduction of an OLMoModel class, and updates to the builder logic, complemented by documentation improvements. Major bugs fixed include correcting the model configuration loading in model-qa.py by switching from model to model_path to ensure the correct model path is used when loading configurations. Overall impact: increased reliability of configuration loading, expanded model compatibility with AMD hardware, and a clearer, more maintainable codebase for QA/model deployment pipelines. Technologies/skills demonstrated include ONNX Runtime integration, Python, model configuration handling, and documentation/testing enhancements.
Activity
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Quality Metrics
Correctness100.0%
Maintainability86.6%
Architecture100.0%
Performance86.6%
AI Usage46.6%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++Deep LearningMachine LearningModel DevelopmentModel OptimizationModel configuration managementPythonPython scriptingQuantization
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