microsoft/onnxruntime-genai
Dec 2025 – Apr 2026
2 Months active
Languages Used
Python
Technical Skills
Deep LearningMachine LearningModel OptimizationQuantizationUnit Testing
Thiago Pereira Rocha
PROFILE
Thiago Pereira Rocha
Worked on the microsoft/onnxruntime-genai repository to enhance quantized model support and reliability. Delivered Quark GPT-OSS integration by implementing QMoE zero point and asymmetric quantization, adding new layers for Quark quantized models, and optimizing projection packing within Experts to improve quantization efficiency and runtime performance. Addressed model loading reliability by introducing a deterministic loading order for lm_head tensors, ensuring weights and biases are processed before quantization parameters. Developed comprehensive unit tests covering multiple edge cases to validate these changes. Demonstrated expertise in Python, deep learning, and quantization, focusing on scalable GenAI deployment and robust model optimization practices.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
906
Activity Months2
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Work History
April 2026
1 Commits
Apr 1, 2026April 2026 monthly summary for microsoft/onnxruntime-genai focusing on reliability and correctness of quantized model loading. Implemented a robust fix for the lm_head tensor loading order in quantized models to address non-deterministic dict iteration. Introduced a new _assign_lm_head_tensors method to ensure weights and biases are processed before quantization parameters, with the loading sequence now deterministic. Added comprehensive unit tests to validate across multiple scenarios.
1 Commits
Apr 1, 2026April 2026 monthly summary for microsoft/onnxruntime-genai focusing on reliability and correctness of quantized model loading. Implemented a robust fix for the lm_head tensor loading order in quantized models to address non-deterministic dict iteration. Introduced a new _assign_lm_head_tensors method to ensure weights and biases are processed before quantization parameters, with the loading sequence now deterministic. Added comprehensive unit tests to validate across multiple scenarios.
April 2026
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025: Delivered Quark GPT-OSS support with QMoE zero point and asymmetric quantization for microsoft/onnxruntime-genai, including new layers for Quark quantized models and packing for gate_up and down_proj inside Experts to boost quantization efficiency and runtime performance. Reference commit: 3be7b360929ab105ab245350d4b0b0e50796cba4 (#1903). No major bugs fixed this month. Impact: enables more efficient, scalable GenAI deployments with improved throughput and resource utilization. Technologies demonstrated: QMoE quantization, Quark quantized model layers, projection packing, and robust commit hygiene.
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025: Delivered Quark GPT-OSS support with QMoE zero point and asymmetric quantization for microsoft/onnxruntime-genai, including new layers for Quark quantized models and packing for gate_up and down_proj inside Experts to boost quantization efficiency and runtime performance. Reference commit: 3be7b360929ab105ab245350d4b0b0e50796cba4 (#1903). No major bugs fixed this month. Impact: enables more efficient, scalable GenAI deployments with improved throughput and resource utilization. Technologies demonstrated: QMoE quantization, Quark quantized model layers, projection packing, and robust commit hygiene.
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Deep LearningMachine LearningModel OptimizationQuantizationUnit Testing
Repositories Contributed To
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