hpcaitech/TensorRT-Model-Optimizer
Sep 2025 – Sep 2025
1 Month active
Languages Used
DockerfilePython
Technical Skills
Deep LearningDockerHugging Face IntegrationMachine LearningModel OptimizationONNX
Riyad Islam
PROFILE
Riyad Islam
Worked on the hpcaitech/TensorRT-Model-Optimizer repository to expand model support and improve deployment reliability for deep learning workflows. Delivered features such as Qwen2.5-VL quantization and Hugging Face export, focusing on model interoperability and runtime precision. Addressed ONNX export fidelity by preventing unwanted autocasting and enhanced inference usability by removing unnecessary training attributes. Simplified the command-line interface and set robust defaults for model optimization, streamlining user experience. Fixed compatibility issues with PyTorch 2.9 and TensorRT/CUDNN, ensuring smoother integration. The work leveraged Python, Docker, and PyTorch, resulting in reduced deployment risk and faster time-to-value for machine learning practitioners.
Overall Statistics
Feature vs Bugs
60%Features
Repository Contributions
6Total
Bugs
2
Commits
6
Features
3
Lines of code
554
Activity Months1
Your Network
1641 peopleSame Organization
@nvidia.com1631
Shared Repositories
10
Work History
September 2025
6 Commits • 3 Features
Sep 1, 2025September 2025 monthly summary for hpcaitech/TensorRT-Model-Optimizer. Focused on expanding model support and improving runtime reliability. Key features delivered include Qwen2.5-VL quantization and HuggingFace export support, along with UI/CLI simplifications and improved precision handling. Major bug fixes improved export fidelity and runtime compatibility. The work enhances deployment reliability, model interoperability, and developer productivity across PyTorch 2.9, TensorRT, and ONNX workflows, delivering tangible business value through reduced risk and faster time-to-value for customers.
6 Commits • 3 Features
Sep 1, 2025September 2025 monthly summary for hpcaitech/TensorRT-Model-Optimizer. Focused on expanding model support and improving runtime reliability. Key features delivered include Qwen2.5-VL quantization and HuggingFace export support, along with UI/CLI simplifications and improved precision handling. Major bug fixes improved export fidelity and runtime compatibility. The work enhances deployment reliability, model interoperability, and developer productivity across PyTorch 2.9, TensorRT, and ONNX workflows, delivering tangible business value through reduced risk and faster time-to-value for customers.
September 2025
Activity
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Quality Metrics
Correctness96.6%
Maintainability90.0%
Architecture90.0%
Performance86.6%
AI Usage80.0%
Skills & Technologies
Programming Languages
DockerfilePython
Technical Skills
Deep LearningDockerHugging Face IntegrationMachine LearningModel OptimizationONNXPyTorchPythonPython Scriptingmachine learningmodel optimization
Repositories Contributed To
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