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Utilities for transferring knowledge from large teacher models to smaller, more efficient student models.
Distinguishing note: Focuses specifically on the compression and transfer process between models, distinct from general model training.
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This 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
Transferring intelligence from large, resource-heavy teacher models into smaller, efficient student models to improve deployment speed and performance.