NVIDIA-NeMo/Automodel
Mar 2026 – Mar 2026
1 Month active
Languages Used
Python
Technical Skills
Deep LearningDistributed ComputingDistributed SystemsMachine LearningModel TrainingPyTorch
Sepehr Sameni
PROFILE
Sepehr Sameni
During March 2026, Sameni enhanced distributed training for knowledge distillation in the NVIDIA-NeMo/Automodel repository. Leveraging Python and PyTorch, Sameni implemented tensor-parallel and pipeline-parallel support for KDLoss, introducing distributed softmax and T² scaling to improve gradient stability across devices. The work included new methods for building teacher models and calculating losses within pipeline-parallel workflows, ensuring seamless integration with existing training pipelines. Sameni expanded unit tests to validate parity between tensor-parallel and pipeline-parallel paths, addressing edge cases and maintaining robustness. This engineering effort laid a solid foundation for scalable, multi-device knowledge distillation and improved experiment throughput in distributed systems.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
1,270
Activity Months1
Your Network
1615 peopleSame Organization
@nvidia.com1559
Work History
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026: NVIDIA-NeMo/Automodel delivered distributed training enhancements for knowledge distillation, enabling scalable KD across tensor-parallel and pipeline-parallel setups. Implemented TP-aware KDLoss with distributed softmax and T² scaling, and added pipeline parallelism for knowledge distillation to improve training throughput. Introduced new methods and wiring to build teacher models and calculate losses within pipeline-parallel KD workflows, ensuring compatibility with existing training pipelines. Expanded unit tests to validate the new functionality and maintain parity with non-TP paths. Laid groundwork for robust, multi-device KD training and improved experiment throughput across device topologies.
2 Commits • 1 Features
Mar 1, 2026March 2026: NVIDIA-NeMo/Automodel delivered distributed training enhancements for knowledge distillation, enabling scalable KD across tensor-parallel and pipeline-parallel setups. Implemented TP-aware KDLoss with distributed softmax and T² scaling, and added pipeline parallelism for knowledge distillation to improve training throughput. Introduced new methods and wiring to build teacher models and calculate losses within pipeline-parallel KD workflows, ensuring compatibility with existing training pipelines. Expanded unit tests to validate the new functionality and maintain parity with non-TP paths. Laid groundwork for robust, multi-device KD training and improved experiment throughput across device topologies.
March 2026
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Deep LearningDistributed ComputingDistributed SystemsMachine LearningModel TrainingPyTorch
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