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Awesome GitHub RepositoriesModel Distillation Methods
Techniques for training smaller student models to mimic the performance of larger teacher models.
Distinguishing note: Focuses on knowledge transfer between models, distinct from standard fine-tuning or architecture design.
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Awesome Model Distillation Methods GitHub Repositories
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- jingyaogong/minimind
jingyaogong/minimind
39,663View on GitHubThis project is a comprehensive framework for the entire lifecycle of transformer-based language models, supporting everything from foundational pretraining to specialized deployment. It provides a modular toolkit for defining neural network architectures, managing data preparation pipelines, and executing training routines across various scales. The framework is designed to handle the full model development process, including supervised fine-tuning, behavioral alignment, and the integration of agentic capabilities. What distinguishes this framework is its focus on efficient training and adva
Smaller student models learn to replicate the output distributions of larger teacher models to achieve high performance with fewer parameters.
Pythonartificial-intelligencelarge-language-model
