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
Rashid Islam contributed to the hpcaitech/TensorRT-Model-Optimizer repository by expanding model support and improving deployment reliability. He implemented Qwen2.5-VL quantization and integrated Hugging Face export, enhancing model interoperability across PyTorch, ONNX, and TensorRT workflows. Using Python and Docker, Rashid refined the ONNX export process to maintain precision and removed unnecessary training attributes to streamline inference. He also simplified the command-line interface and set robust defaults for model optimization, improving usability for developers. His work addressed compatibility issues with PyTorch 2.9 and TensorRT/CUDNN, resulting in more reliable runtime behavior and faster, lower-risk deployment for machine learning models.
Overall Statistics
Feature vs Bugs
60%Features
Repository Contributions
6Total
Bugs
2
Commits
6
Features
3
Lines of code
554
Activity Months1
Your Network
1353 people
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
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