thinking-machines-lab/tinker-cookbook
Apr 2026 – Apr 2026
1 Month active
Languages Used
Python
Technical Skills
Asynchronous ProgrammingDeep LearningMachine LearningPython
Kenneth Li
PROFILE
Kenneth Li
Worked on the thinking-machines-lab/tinker-cookbook repository to enhance student-teacher distillation workflows by implementing reverse KL divergence loss training. Developed and integrated a reverse KL divergence loss calculation into the training pipeline, enabling analytical reverse KL training over teacher top-K distributions. Addressed a critical issue in the reverse KL computation, collaborating with another contributor to resolve the bug and ensure training stability. Utilized Python, deep learning, and asynchronous programming to build reverse KL datum generation and support end-to-end distillation. Verified the new approach with comprehensive tests and documentation updates, improving model calibration and data efficiency for machine learning distillation tasks.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
530
Activity Months1
Your Network
60 peopleWork History
April 2026
1 Commits • 1 Features
Apr 1, 2026Apr 2026 monthly summary for thinking-machines-lab/tinker-cookbook: Delivered end-to-end enhancement for student-teacher distillation via reverse KL divergence. Implemented reverse KL divergence loss calculation, added reverse KL datum building, and integrated into the training pipeline to enable analytical reverse KL training over teacher top-K distributions. Also fixed a critical issue in reverse KL flow (see commit 869911ed2db38ed891e56c3fac7c6ceda5c6aae3) as part of reverse KL fix (#677). These changes improve model calibration, training stability, and data-efficiency for distillation workflows. Collaboration with Claude Opus on the fix.
1 Commits • 1 Features
Apr 1, 2026Apr 2026 monthly summary for thinking-machines-lab/tinker-cookbook: Delivered end-to-end enhancement for student-teacher distillation via reverse KL divergence. Implemented reverse KL divergence loss calculation, added reverse KL datum building, and integrated into the training pipeline to enable analytical reverse KL training over teacher top-K distributions. Also fixed a critical issue in reverse KL flow (see commit 869911ed2db38ed891e56c3fac7c6ceda5c6aae3) as part of reverse KL fix (#677). These changes improve model calibration, training stability, and data-efficiency for distillation workflows. Collaboration with Claude Opus on the fix.
April 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Asynchronous ProgrammingDeep LearningMachine LearningPython
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