zabojeb/mts-fast-llms
Jul 2025 – Jul 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Basic File ManagementDeep LearningHugging Face TransformersKnowledge DistillationLLMMachine Learning
Mark729k
PROFILE
Mark729k
Mark Khusnutdinov developed core features for the zabojeb/mts-fast-llms repository, focusing on model efficiency and maintainability. He built a Knowledge Distillation Framework with a custom DistillationTrainer and loss function, enabling end-to-end student-teacher training with integrated metric tracking in Python and PyTorch. Mark also implemented a comprehensive LLM Pruning Framework, supporting magnitude-based, structured, and random pruning with iterative workflows and post-pruning calibration to optimize large language models. His work included repository scaffolding, research notebook setup, and code cleanup, which improved onboarding and code quality. The depth of his contributions accelerated reproducible experimentation and streamlined model optimization workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
3
Lines of code
989
Activity Months1
Your Network
32 people
Work History
July 2025
6 Commits • 3 Features
Jul 1, 2025July 2025 performance summary for zabojeb/mts-fast-llms: Key features delivered include a Knowledge Distillation Framework with a DistillationTrainer class and distillation_loss function, enabling end-to-end student-teacher training with training/validation loops and metric tracking. A comprehensive LLM Pruning Framework was added, supporting magnitude-based, structured, and random pruning with iterative application and post-pruning calibration to reduce model size and compute while preserving accuracy. Repository scaffolding and cleanup were completed, including a research notebook placeholder and a main script placeholder, along with cleanup of unused files to improve onboarding and maintainability. No major customer-reported bugs were identified; internal stability and code hygiene improvements were implemented to reduce technical debt and improve reliability of experimentation. Overall impact: accelerated experimentation with distillation and pruning workflows, improved model efficiency, and a cleaner, more maintainable codebase. Technologies/skills demonstrated: Python, training loop design, custom loss functions, distillation techniques, multiple pruning strategies, iterative pruning workflows, post-pruning calibration, and project scaffolding.
6 Commits • 3 Features
Jul 1, 2025July 2025 performance summary for zabojeb/mts-fast-llms: Key features delivered include a Knowledge Distillation Framework with a DistillationTrainer class and distillation_loss function, enabling end-to-end student-teacher training with training/validation loops and metric tracking. A comprehensive LLM Pruning Framework was added, supporting magnitude-based, structured, and random pruning with iterative application and post-pruning calibration to reduce model size and compute while preserving accuracy. Repository scaffolding and cleanup were completed, including a research notebook placeholder and a main script placeholder, along with cleanup of unused files to improve onboarding and maintainability. No major customer-reported bugs were identified; internal stability and code hygiene improvements were implemented to reduce technical debt and improve reliability of experimentation. Overall impact: accelerated experimentation with distillation and pruning workflows, improved model efficiency, and a cleaner, more maintainable codebase. Technologies/skills demonstrated: Python, training loop design, custom loss functions, distillation techniques, multiple pruning strategies, iterative pruning workflows, post-pruning calibration, and project scaffolding.
July 2025
Activity
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Quality Metrics
Correctness65.0%
Maintainability63.4%
Architecture65.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Basic File ManagementDeep LearningHugging Face TransformersKnowledge DistillationLLMMachine LearningModel OptimizationModel PruningPyTorchTransformers
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