nvidia-cosmos/cosmos-transfer1
Aug 2025 – Aug 2025
1 Month active
Languages Used
PythonShell
Technical Skills
Computer VisionConfiguration Management (Hydra)ControlNetDeep LearningDistributed TrainingFSDP
Grace Lam
PROFILE
Grace Lam
Grace Slam developed distillation training support for the nvidia-cosmos/cosmos-transfer1 repository, enabling efficient single-step model distillation from larger, multi-step models. She designed and implemented changes across configuration management using Hydra, model architecture, dataset handling, and inference utilities, ensuring seamless integration of the new training paradigm. Her work leveraged deep learning techniques and distributed training with PyTorch, focusing on model distillation and transformer architectures. By addressing both the technical and practical aspects of distillation, Grace delivered a robust feature that streamlines the process of creating compact models, demonstrating depth in both engineering execution and understanding of advanced machine learning workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
7,658
Activity Months1
Your Network
23 people
Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for nvidia-cosmos/cosmos-transfer1 focusing on key technical achievements and business value.
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for nvidia-cosmos/cosmos-transfer1 focusing on key technical achievements and business value.
August 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
PythonShell
Technical Skills
Computer VisionConfiguration Management (Hydra)ControlNetDeep LearningDistributed TrainingFSDPModel DistillationPyTorchTransformer ArchitectureVideo Processing
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