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Methods for refining model behavior to match human preferences and intent.
Distinguishing note: Focuses on post-training alignment and preference optimization rather than initial pre-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
Model behavior is aligned with human intent by mathematically maximizing the probability of preferred responses over rejected ones.