nv-auto-deploy/TensorRT-LLM
Apr 2025 – Apr 2025
1 Month active
Languages Used
C++Python
Technical Skills
C++Deep LearningExecutorFull Stack DevelopmentLoRAModel Optimization
shaharmor98
PROFILE
Shaharmor98
During April 2025, Smor worked on integrating LoRA and PEFT adaptation into the nv-auto-deploy/TensorRT-LLM repository, enabling adapter-based model experimentation and deployment. Smor developed an end-to-end LoRA flow, updating both C++ bindings and Python configuration to support LoRA parameters and PEFT caching within the PyExecutor and TensorRT-LLM frameworks. This work involved cross-language integration, resource management, and model optimization, allowing LoRA adapters to be loaded and executed efficiently from model initialization through inference. The resulting architecture improved deployment flexibility and experimentation speed, demonstrating depth in deep learning, executor design, and full stack development across C++ and Python environments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
857
Activity Months1
Your Network
1343 people
Work History
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025: Focused on enabling LoRA/PEFT adaptation across PyExecutor and TensorRT-LLM to accelerate experimentation and deployment of adapter-based models. Delivered end-to-end LoRA flow, enhanced PEFT caching, and updated core components (C++ bindings and Python config) to support LoRA parameters, driving faster time-to-value for inference deployments and greater modeling flexibility.
2 Commits • 1 Features
Apr 1, 2025April 2025: Focused on enabling LoRA/PEFT adaptation across PyExecutor and TensorRT-LLM to accelerate experimentation and deployment of adapter-based models. Delivered end-to-end LoRA flow, enhanced PEFT caching, and updated core components (C++ bindings and Python config) to support LoRA parameters, driving faster time-to-value for inference deployments and greater modeling flexibility.
April 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++Deep LearningExecutorFull Stack DevelopmentLoRAModel OptimizationPEFTPybindPythonResource ManagementTensorRT
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