google-research/timesfm
Apr 2026 – Apr 2026
1 Month active
Languages Used
BashMarkdownPythonYAML
Technical Skills
CI/CDCode ReviewData ScienceDeep LearningDistributed ComputingGitHub Actions
darkpowerxo
PROFILE
Darkpowerxo
Developed a scalable, production-ready PEFT-based fine-tuning ecosystem for TimesFM 2.5 within the google-research/timesfm repository, focusing on robust model tuning and data integrity. Designed and implemented adapter-based fine-tuning using LoRA and DoRA, a sliding-window TimeSeriesDataset, and a multi-GPU PEFT trainer, all accessible via a command line interface. Enhanced data handling to prevent leakage through per-input normalization and per-time-series ridge regression. Upgraded CI workflows with GitHub Actions and expanded unit test coverage for improved stability. Leveraged Python, PyTorch, and Transformers to deliver maintainable code, clearer documentation, and a foundation for scalable, reliable time series model development.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
17Total
Bugs
0
Commits
17
Features
3
Lines of code
5,339
Activity Months1
Work History
April 2026
17 Commits • 3 Features
Apr 1, 2026April 2026 summary focused on delivering a scalable, production-ready PEFT-based fine-tuning ecosystem for TimesFM 2.5, strengthening model tuning capabilities, data integrity, and project reliability. Key outcomes include a feature-rich PEFT training pipeline, robust data handling to prevent leakage, and improvements to documentation and CI that stabilize downstream workflows and accelerate onboarding.
17 Commits • 3 Features
Apr 1, 2026April 2026 summary focused on delivering a scalable, production-ready PEFT-based fine-tuning ecosystem for TimesFM 2.5, strengthening model tuning capabilities, data integrity, and project reliability. Key outcomes include a feature-rich PEFT training pipeline, robust data handling to prevent leakage, and improvements to documentation and CI that stabilize downstream workflows and accelerate onboarding.
April 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability91.8%
Architecture96.6%
Performance90.6%
AI Usage38.8%
Skills & Technologies
Programming Languages
BashMarkdownPythonYAML
Technical Skills
CI/CDCode ReviewData ScienceDeep LearningDistributed ComputingGitHub ActionsJAXLoggingMachine LearningPyTorchPythonPython programmingTransformerscommand line interfacecommunity engagement
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