linkedin/Liger-Kernel
Oct 2025 – Oct 2025
1 Month active
Languages Used
Python
Technical Skills
Deep LearningMachine LearningModel OptimizationPythonTransformer Models
Alena Wang
PROFILE
Alena Wang
Worked on backend enhancements for kubeflow/pipelines and LinkedIn’s Liger-Kernel, focusing on reliability and debugging for machine learning workflows. Delivered a feature in kubeflow/pipelines to publish executor logs as artifacts when components fail, improving visibility and reducing mean time to resolution. Updated test coverage to ensure logs are correctly uploaded and accessible. In Liger-Kernel, implemented precise swiglu patch targeting for Llama4 MoE layers, reducing runtime risks by patching shared_expert modules within MoE layers and updating default parameters. Utilized Python, Go, and deep learning model optimization techniques, demonstrating a methodical approach to backend development, logging, and transformer model stability.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
138
Activity Months2
Your Network
123 peopleWork History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for kubeflow/pipelines focusing on KFPv2 failed component logging enhancement and related test coverage. Delivered backend changes to publish executor logs for failed components, improved artifact handling, and updated tests to verify log upload and accessibility. These changes enhance debugging visibility, reduce MTTR, and strengthen reliability for KFPv2 workflows.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for kubeflow/pipelines focusing on KFPv2 failed component logging enhancement and related test coverage. Delivered backend changes to publish executor logs for failed components, improved artifact handling, and updated tests to verify log upload and accessibility. These changes enhance debugging visibility, reduce MTTR, and strengthen reliability for KFPv2 workflows.
February 2026
October 2025
1 Commits
Oct 1, 2025October 2025 focused on stabilizing swiglu patching for Llama4 MoE layers in LinkedIn's Liger-Kernel. Implemented a precise patch-targeting approach that patches shared_expert within MoE layers and patches non-MoE layers directly, and updated the default swiglu parameter to True. This change reduces patching misconfigurations, lowers runtime risk in MoE configurations, and supports safer deployment and experimentation with Llama4 architectures. Commit reference documented for traceability: fix(llama4): Get correct swiglu patch target for llama4 moe layer (#907).
October 2025
1 Commits
Oct 1, 2025October 2025 focused on stabilizing swiglu patching for Llama4 MoE layers in LinkedIn's Liger-Kernel. Implemented a precise patch-targeting approach that patches shared_expert within MoE layers and patches non-MoE layers directly, and updated the default swiglu parameter to True. This change reduces patching misconfigurations, lowers runtime risk in MoE configurations, and supports safer deployment and experimentation with Llama4 architectures. Commit reference documented for traceability: fix(llama4): Get correct swiglu patch target for llama4 moe layer (#907).
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
GoPython
Technical Skills
Deep LearningMachine LearningModel OptimizationPythonTransformer Modelsbackend developmentloggingtesting
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
kubeflow/pipelines
Feb 2026 – Feb 2026
1 Month active
Languages Used
Go
Technical Skills
backend developmentloggingtesting