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anujj

PROFILE

Anujj

Worked on microsoft/Olive and microsoft/onnxruntime-genai, focusing on quantization integration, execution provider configuration, and model optimization for deep learning inference. Delivered AWQ INT4 quantization and enhanced the TensorRT Model Optimizer workflow, improving deployment stability and low-precision readiness using Python and ONNX Runtime. Refactored provider naming to NvTensorRtRtx for consistent branding and updated the ModelBuilder API, reducing user confusion. Added TRT-RTX execution provider support with enforced QDQ quantization defaults, aligning CLI parsing and model builder logic for GenAI workloads. Updated documentation and dependencies, streamlining Windows RTX deployments and ensuring clearer guidance for quantized inference and workflow integration.

Overall Statistics

Feature vs Bugs

100%Features

Repository Contributions

4Total
Bugs
0
Commits
4
Features
3
Lines of code
645
Activity Months3

Work History

October 2025

1 Commits • 1 Features

Oct 1, 2025

October 2025 (microsoft/onnxruntime-genai): Delivered TRT-RTX execution provider support with a user-facing NvTensorRtRtx alias and enforced default QDQ quantization when TRT-RTX is selected. This improves quantization path consistency, stabilizes model-building behavior, and aligns CLI argument parsing with TRT-RTX usage. The work enhances reliability and integration for GenAI workloads and sets the foundation for further TRT-RTX optimizations.

September 2025

1 Commits • 1 Features

Sep 1, 2025

Monthly summary for 2025-09: Implemented user-facing name alignment for NvTensorRTRTXExecutionProvider in microsoft/Olive, renaming to NvTensorRtRtx to reflect GenAI naming discussions and ensure consistent branding in the ModelBuilder API. This refactor aligns internal representation with external naming, reducing user confusion and improving clarity for downstream integrations across Olive workflows.

November 2024

2 Commits • 1 Features

Nov 1, 2024

November 2024 highlights for microsoft/Olive: Delivered refined AWQ INT4 quantization integration and enhanced TensorRT Model Optimizer workflow, improving low-precision inference readiness and deployment stability. Removed outdated BERT example, added Phi-3 model example, and tightened opset version handling. Updated dependencies and documentation to streamline Windows RTX deployments. Business impact: reduced setup friction, faster model optimization cycles, and clearer guidance for quantized inference on Windows hardware.

Activity

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Quality Metrics

Correctness87.6%
Maintainability85.0%
Architecture85.0%
Performance80.0%
AI Usage20.0%

Skills & Technologies

Programming Languages

BashMarkdownPythonShell

Technical Skills

Deep Learning FrameworksDocumentationExecution Provider ConfigurationModel OptimizationONNX RuntimePythonQuantizationRefactoring

Repositories Contributed To

2 repos

Overview of all repositories you've contributed to across your timeline

microsoft/Olive

Nov 2024 Sep 2025
2 Months active

Languages Used

BashMarkdownPythonShell

Technical Skills

Deep Learning FrameworksDocumentationModel OptimizationONNX RuntimePythonQuantization

microsoft/onnxruntime-genai

Oct 2025 Oct 2025
1 Month active

Languages Used

Python

Technical Skills

Execution Provider ConfigurationModel OptimizationQuantization